Initial commit

models/CifarDenseNet.py

@@ -0,0 +1,105 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .initialization import initialize_resnet
+
+
+class Bottleneck(nn.Module):
+  def __init__(self, nChannels, growthRate):
+    super(Bottleneck, self).__init__()
+    interChannels = 4*growthRate
+    self.bn1 = nn.BatchNorm2d(nChannels)
+    self.conv1 = nn.Conv2d(nChannels, interChannels, kernel_size=1, bias=False)
+    self.bn2 = nn.BatchNorm2d(interChannels)
+    self.conv2 = nn.Conv2d(interChannels, growthRate, kernel_size=3, padding=1, bias=False)
+
+  def forward(self, x):
+    out = self.conv1(F.relu(self.bn1(x)))
+    out = self.conv2(F.relu(self.bn2(out)))
+    out = torch.cat((x, out), 1)
+    return out
+
+
+class SingleLayer(nn.Module):
+  def __init__(self, nChannels, growthRate):
+    super(SingleLayer, self).__init__()
+    self.bn1 = nn.BatchNorm2d(nChannels)
+    self.conv1 = nn.Conv2d(nChannels, growthRate, kernel_size=3, padding=1, bias=False)
+
+  def forward(self, x):
+    out = self.conv1(F.relu(self.bn1(x)))
+    out = torch.cat((x, out), 1)
+    return out
+
+
+class Transition(nn.Module):
+  def __init__(self, nChannels, nOutChannels):
+    super(Transition, self).__init__()
+    self.bn1 = nn.BatchNorm2d(nChannels)
+    self.conv1 = nn.Conv2d(nChannels, nOutChannels, kernel_size=1, bias=False)
+
+  def forward(self, x):
+    out = self.conv1(F.relu(self.bn1(x)))
+    out = F.avg_pool2d(out, 2)
+    return out
+
+
+class DenseNet(nn.Module):
+  def __init__(self, growthRate, depth, reduction, nClasses, bottleneck):
+    super(DenseNet, self).__init__()
+
+    if bottleneck:  nDenseBlocks = int( (depth-4) / 6 )
+    else         :  nDenseBlocks = int( (depth-4) / 3 )
+
+    self.message = 'CifarDenseNet : block : {:}, depth : {:}, reduction : {:}, growth-rate = {:}, class = {:}'.format('bottleneck' if bottleneck else 'basic', depth, reduction, growthRate, nClasses)
+
+    nChannels = 2*growthRate
+    self.conv1 = nn.Conv2d(3, nChannels, kernel_size=3, padding=1, bias=False)
+
+    self.dense1 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck)
+    nChannels += nDenseBlocks*growthRate
+    nOutChannels = int(math.floor(nChannels*reduction))
+    self.trans1 = Transition(nChannels, nOutChannels)
+
+    nChannels = nOutChannels
+    self.dense2 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck)
+    nChannels += nDenseBlocks*growthRate
+    nOutChannels = int(math.floor(nChannels*reduction))
+    self.trans2 = Transition(nChannels, nOutChannels)
+
+    nChannels = nOutChannels
+    self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck)
+    nChannels += nDenseBlocks*growthRate
+
+    self.act = nn.Sequential(
+                  nn.BatchNorm2d(nChannels), nn.ReLU(inplace=True),
+                  nn.AvgPool2d(8))
+    self.fc  = nn.Linear(nChannels, nClasses)
+
+    self.apply(initialize_resnet)
+
+  def get_message(self):
+    return self.message
+
+  def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck):
+    layers = []
+    for i in range(int(nDenseBlocks)):
+      if bottleneck:
+        layers.append(Bottleneck(nChannels, growthRate))
+      else:
+        layers.append(SingleLayer(nChannels, growthRate))
+      nChannels += growthRate
+    return nn.Sequential(*layers)
+
+  def forward(self, inputs):
+    out = self.conv1( inputs )
+    out = self.trans1(self.dense1(out))
+    out = self.trans2(self.dense2(out))
+    out = self.dense3(out)
+    features = self.act(out)
+    features = features.view(features.size(0), -1)
+    out = self.fc(features)
+    return features, out

models/CifarResNet.py

@@ -0,0 +1,157 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .initialization import initialize_resnet
+from .SharedUtils    import additive_func
+
+
+class Downsample(nn.Module):  
+
+  def __init__(self, nIn, nOut, stride):
+    super(Downsample, self).__init__() 
+    assert stride == 2 and nOut == 2*nIn, 'stride:{} IO:{},{}'.format(stride, nIn, nOut)
+    self.in_dim  = nIn
+    self.out_dim = nOut
+    self.avg  = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)   
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=1, padding=0, bias=False)
+
+  def forward(self, x):
+    x   = self.avg(x)
+    out = self.conv(x)
+    return out
+
+
+class ConvBNReLU(nn.Module):
+  
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, relu):
+    super(ConvBNReLU, self).__init__()
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, bias=bias)
+    self.bn   = nn.BatchNorm2d(nOut)
+    if relu: self.relu = nn.ReLU(inplace=True)
+    else   : self.relu = None
+    self.out_dim = nOut
+    self.num_conv = 1
+
+  def forward(self, x):
+    conv = self.conv( x )
+    bn   = self.bn( conv )
+    if self.relu: return self.relu( bn )
+    else        : return bn
+
+
+class ResNetBasicblock(nn.Module):
+  expansion = 1
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, True)
+    self.conv_b = ConvBNReLU(  planes, planes, 3,      1, 1, False, False)
+    if stride == 2:
+      self.downsample = Downsample(inplanes, planes, stride)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, False)
+    else:
+      self.downsample = None
+    self.out_dim = planes
+    self.num_conv = 2
+
+  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
+    out = additive_func(residual, basicblock)
+    return F.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_1x1 = ConvBNReLU(inplanes, planes, 1,      1, 0, False, True)
+    self.conv_3x3 = ConvBNReLU(  planes, planes, 3, stride, 1, False, True)
+    self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, False)
+    if stride == 2:
+      self.downsample = Downsample(inplanes, planes*self.expansion, stride)
+    elif inplanes != planes*self.expansion:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, False)
+    else:
+      self.downsample = None
+    self.out_dim = planes * self.expansion
+    self.num_conv = 3
+
+  def forward(self, inputs):
+
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    out = additive_func(residual, bottleneck)
+    return F.relu(out, inplace=True)
+
+
+
+class CifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, num_classes, zero_init_residual):
+    super(CifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+
+    self.message     = 'CifarResNet : Block : {:}, Depth : {:}, Layers for each block : {:}'.format(block_name, depth, layer_blocks)
+    self.num_classes = num_classes
+    self.channels    = [16]
+    self.layers      = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, True) ] )
+    for stage in range(3):
+      for iL in range(layer_blocks):
+        iC     = self.channels[-1]
+        planes = 16 * (2**stage)
+        stride = 2 if stage > 0 and iL == 0 else 1
+        module = block(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
+
+    self.avgpool = nn.AvgPool2d(8)
+    self.classifier = nn.Linear(module.out_dim, num_classes)
+    assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+
+    self.apply(initialize_resnet)
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, ResNetBasicblock):
+          nn.init.constant_(m.conv_b.bn.weight, 0)
+        elif isinstance(m, ResNetBottleneck):
+          nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/CifarWideResNet.py

@@ -0,0 +1,94 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .initialization import initialize_resnet
+
+
+class WideBasicblock(nn.Module):
+  def __init__(self, inplanes, planes, stride, dropout=False):
+    super(WideBasicblock, self).__init__()
+
+    self.bn_a = nn.BatchNorm2d(inplanes)
+    self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+    self.bn_b = nn.BatchNorm2d(planes)
+    if dropout:
+      self.dropout = nn.Dropout2d(p=0.5, inplace=True)
+    else:
+      self.dropout = None
+    self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+
+    if inplanes != planes:
+      self.downsample = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False)
+    else:
+      self.downsample = None
+
+  def forward(self, x):
+
+    basicblock = self.bn_a(x)
+    basicblock = F.relu(basicblock)
+    basicblock = self.conv_a(basicblock)
+
+    basicblock = self.bn_b(basicblock)
+    basicblock = F.relu(basicblock)
+    if self.dropout is not None:
+      basicblock = self.dropout(basicblock)
+    basicblock = self.conv_b(basicblock)
+
+    if self.downsample is not None:
+      x = self.downsample(x)
+    
+    return x + basicblock
+
+
+class CifarWideResNet(nn.Module):
+  """
+  ResNet optimized for the Cifar dataset, as specified in
+  https://arxiv.org/abs/1512.03385.pdf
+  """
+  def __init__(self, depth, widen_factor, num_classes, dropout):
+    super(CifarWideResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    assert (depth - 4) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+    layer_blocks = (depth - 4) // 6
+    print ('CifarPreResNet : Depth : {} , Layers for each block : {}'.format(depth, layer_blocks))
+
+    self.num_classes = num_classes
+    self.dropout = dropout
+    self.conv_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
+
+    self.message  = 'Wide ResNet : depth={:}, widen_factor={:}, class={:}'.format(depth, widen_factor, num_classes)
+    self.inplanes = 16
+    self.stage_1 = self._make_layer(WideBasicblock, 16*widen_factor, layer_blocks, 1)
+    self.stage_2 = self._make_layer(WideBasicblock, 32*widen_factor, layer_blocks, 2)
+    self.stage_3 = self._make_layer(WideBasicblock, 64*widen_factor, layer_blocks, 2)
+    self.lastact = nn.Sequential(nn.BatchNorm2d(64*widen_factor), nn.ReLU(inplace=True))
+    self.avgpool = nn.AvgPool2d(8)
+    self.classifier = nn.Linear(64*widen_factor, num_classes)
+
+    self.apply(initialize_resnet)
+
+  def get_message(self):
+    return self.message
+
+  def _make_layer(self, block, planes, blocks, stride):
+
+    layers = []
+    layers.append(block(self.inplanes, planes, stride, self.dropout))
+    self.inplanes = planes
+    for i in range(1, blocks):
+      layers.append(block(self.inplanes, planes, 1, self.dropout))
+
+    return nn.Sequential(*layers)
+
+  def forward(self, x):
+    x = self.conv_3x3(x)
+    x = self.stage_1(x)
+    x = self.stage_2(x)
+    x = self.stage_3(x)
+    x = self.lastact(x)
+    x = self.avgpool(x)
+    features = x.view(x.size(0), -1)
+    outs     = self.classifier(features)
+    return features, outs

models/ImageNet_MobileNetV2.py

@@ -0,0 +1,101 @@
+# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
+from torch import nn
+from .initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+  def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
+    super(ConvBNReLU, self).__init__()
+    padding = (kernel_size - 1) // 2
+    self.conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False)
+    self.bn   = nn.BatchNorm2d(out_planes)
+    self.relu = nn.ReLU6(inplace=True)
+  
+  def forward(self, x):
+    out = self.conv( x )
+    out = self.bn  ( out )
+    out = self.relu( out )
+    return out
+
+
+class InvertedResidual(nn.Module):
+  def __init__(self, inp, oup, stride, expand_ratio):
+    super(InvertedResidual, self).__init__()
+    self.stride = stride
+    assert stride in [1, 2]
+
+    hidden_dim = int(round(inp * expand_ratio))
+    self.use_res_connect = self.stride == 1 and inp == oup
+
+    layers = []
+    if expand_ratio != 1:
+      # pw
+      layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
+    layers.extend([
+      # dw
+      ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
+      # pw-linear
+      nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+      nn.BatchNorm2d(oup),
+    ])
+    self.conv = nn.Sequential(*layers)
+
+  def forward(self, x):
+    if self.use_res_connect:
+      return x + self.conv(x)
+    else:
+      return self.conv(x)
+
+
+class MobileNetV2(nn.Module):
+  def __init__(self, num_classes, width_mult, input_channel, last_channel, block_name, dropout):
+    super(MobileNetV2, self).__init__()
+    if block_name == 'InvertedResidual':
+      block = InvertedResidual
+    else:
+      raise ValueError('invalid block name : {:}'.format(block_name))
+    inverted_residual_setting = [
+      # t, c,  n, s
+      [1, 16 , 1, 1],
+      [6, 24 , 2, 2],
+      [6, 32 , 3, 2],
+      [6, 64 , 4, 2],
+      [6, 96 , 3, 1],
+      [6, 160, 3, 2],
+      [6, 320, 1, 1],
+    ]
+
+    # building first layer
+    input_channel = int(input_channel * width_mult)
+    self.last_channel = int(last_channel * max(1.0, width_mult))
+    features = [ConvBNReLU(3, input_channel, stride=2)]
+    # building inverted residual blocks
+    for t, c, n, s in inverted_residual_setting:
+      output_channel = int(c * width_mult)
+      for i in range(n):
+        stride = s if i == 0 else 1
+        features.append(block(input_channel, output_channel, stride, expand_ratio=t))
+        input_channel = output_channel
+    # building last several layers
+    features.append(ConvBNReLU(input_channel, self.last_channel, kernel_size=1))
+    # make it nn.Sequential
+    self.features = nn.Sequential(*features)
+
+    # building classifier
+    self.classifier = nn.Sequential(
+      nn.Dropout(dropout),
+      nn.Linear(self.last_channel, num_classes),
+    )
+    self.message = 'MobileNetV2 : width_mult={:}, in-C={:}, last-C={:}, block={:}, dropout={:}'.format(width_mult, input_channel, last_channel, block_name, dropout)
+
+    # weight initialization
+    self.apply( initialize_resnet )
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    features = self.features(inputs)
+    vectors  = features.mean([2, 3])
+    predicts = self.classifier(vectors)
+    return features, predicts

models/ImageNet_ResNet.py

@@ -0,0 +1,172 @@
+# Deep Residual Learning for Image Recognition, CVPR 2016
+import torch.nn as nn
+from .initialization import initialize_resnet
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1):
+  return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+  return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+  expansion = 1
+
+  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64):
+    super(BasicBlock, self).__init__()
+    if groups != 1 or base_width != 64:
+      raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+    # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+    self.conv1 = conv3x3(inplanes, planes, stride)
+    self.bn1   = nn.BatchNorm2d(planes)
+    self.relu  = nn.ReLU(inplace=True)
+    self.conv2 = conv3x3(planes, planes)
+    self.bn2   = nn.BatchNorm2d(planes)
+    self.downsample = downsample
+    self.stride = stride
+
+  def forward(self, x):
+    identity = x
+
+    out = self.conv1(x)
+    out = self.bn1(out)
+    out = self.relu(out)
+
+    out = self.conv2(out)
+    out = self.bn2(out)
+
+    if self.downsample is not None:
+      identity = self.downsample(x)
+
+    out += identity
+    out = self.relu(out)
+
+    return out
+
+
+class Bottleneck(nn.Module):
+  expansion = 4
+
+  def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64):
+    super(Bottleneck, self).__init__()
+    width = int(planes * (base_width / 64.)) * groups
+    # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+    self.conv1 = conv1x1(inplanes, width)
+    self.bn1   = nn.BatchNorm2d(width)
+    self.conv2 = conv3x3(width, width, stride, groups)
+    self.bn2   = nn.BatchNorm2d(width)
+    self.conv3 = conv1x1(width, planes * self.expansion)
+    self.bn3   = nn.BatchNorm2d(planes * self.expansion)
+    self.relu  = nn.ReLU(inplace=True)
+    self.downsample = downsample
+    self.stride = stride
+
+  def forward(self, x):
+    identity = x
+
+    out = self.conv1(x)
+    out = self.bn1(out)
+    out = self.relu(out)
+
+    out = self.conv2(out)
+    out = self.bn2(out)
+    out = self.relu(out)
+
+    out = self.conv3(out)
+    out = self.bn3(out)
+
+    if self.downsample is not None:
+      identity = self.downsample(x)
+
+    out += identity
+    out = self.relu(out)
+
+    return out
+
+
+class ResNet(nn.Module):
+
+  def __init__(self, block_name, layers, deep_stem, num_classes, zero_init_residual, groups, width_per_group):
+    super(ResNet, self).__init__()
+
+    #planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
+    if block_name == 'BasicBlock'  : block= BasicBlock
+    elif block_name == 'Bottleneck': block= Bottleneck
+    else                           : raise ValueError('invalid block-name : {:}'.format(block_name))
+
+    if not deep_stem:
+      self.conv = nn.Sequential(
+                   nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
+                   nn.BatchNorm2d(64), nn.ReLU(inplace=True))
+    else:
+      self.conv = nn.Sequential(
+                   nn.Conv2d(           3, 32, kernel_size=3, stride=2, padding=1, bias=False),
+                   nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+                   nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
+                   nn.BatchNorm2d(32), nn.ReLU(inplace=True),
+                   nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
+                   nn.BatchNorm2d(64), nn.ReLU(inplace=True))
+    self.inplanes = 64
+    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+    self.layer1 = self._make_layer(block, 64 , layers[0], stride=1, groups=groups, base_width=width_per_group)
+    self.layer2 = self._make_layer(block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group)
+    self.layer3 = self._make_layer(block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group)
+    self.layer4 = self._make_layer(block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group)
+    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+    self.fc      = nn.Linear(512 * block.expansion, num_classes)
+    self.message = 'block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}'.format(block, layers, deep_stem, num_classes)
+
+    self.apply( initialize_resnet )
+
+    # Zero-initialize the last BN in each residual branch,
+    # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+    # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, Bottleneck):
+          nn.init.constant_(m.bn3.weight, 0)
+        elif isinstance(m, BasicBlock):
+          nn.init.constant_(m.bn2.weight, 0)
+
+  def _make_layer(self, block, planes, blocks, stride, groups, base_width):
+    downsample = None
+    if stride != 1 or self.inplanes != planes * block.expansion:
+      if stride == 2:
+        downsample = nn.Sequential(
+          nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+          conv1x1(self.inplanes, planes * block.expansion, 1),
+          nn.BatchNorm2d(planes * block.expansion),
+        )
+      elif stride == 1:
+        downsample = nn.Sequential(
+          conv1x1(self.inplanes, planes * block.expansion, stride),
+          nn.BatchNorm2d(planes * block.expansion),
+        )
+      else: raise ValueError('invalid stride [{:}] for downsample'.format(stride))
+
+    layers = []
+    layers.append(block(self.inplanes, planes, stride, downsample, groups, base_width))
+    self.inplanes = planes * block.expansion
+    for _ in range(1, blocks):
+      layers.append(block(self.inplanes, planes, 1, None, groups, base_width))
+
+    return nn.Sequential(*layers)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, x):
+    x = self.conv(x)
+    x = self.maxpool(x)
+
+    x = self.layer1(x)
+    x = self.layer2(x)
+    x = self.layer3(x)
+    x = self.layer4(x)
+
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.fc(features)
+
+    return features, logits

models/SharedUtils.py

@@ -0,0 +1,34 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch
+import torch.nn as nn
+
+
+def additive_func(A, B):
+  assert A.dim() == B.dim() and A.size(0) == B.size(0), '{:} vs {:}'.format(A.size(), B.size())
+  C = min(A.size(1), B.size(1))
+  if A.size(1) == B.size(1):
+    return A + B
+  elif A.size(1) < B.size(1):
+    out = B.clone()
+    out[:,:C] += A
+    return out
+  else:
+    out = A.clone()
+    out[:,:C] += B
+    return out
+
+
+def change_key(key, value):
+  def func(m):
+    if hasattr(m, key):
+      setattr(m, key, value)
+  return func
+
+
+def parse_channel_info(xstring):
+  blocks = xstring.split(' ')
+  blocks = [x.split('-') for x in blocks]
+  blocks = [[int(_) for _ in x] for x in blocks]
+  return blocks

models/__init__.py

@@ -0,0 +1,185 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from os import path as osp
+from typing import List, Text
+import torch
+
+__all__ = ['change_key', 'get_cell_based_tiny_net', 'get_search_spaces', 'get_cifar_models', 'get_imagenet_models', \
+           'obtain_model', 'obtain_search_model', 'load_net_from_checkpoint', \
+           'CellStructure', 'CellArchitectures'
+           ]
+
+# useful modules
+from config_utils import dict2config
+from .SharedUtils import change_key
+from .cell_searchs import CellStructure, CellArchitectures
+
+
+# Cell-based NAS Models
+def get_cell_based_tiny_net(config):
+  if isinstance(config, dict): config = dict2config(config, None) # to support the argument being a dict
+  super_type = getattr(config, 'super_type', 'basic')
+  group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
+  if super_type == 'basic' and config.name in group_names:
+    from .cell_searchs import nas201_super_nets as nas_super_nets
+    try:
+      return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space, config.affine, config.track_running_stats)
+    except:
+      return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space)
+  elif super_type == 'nasnet-super':
+    from .cell_searchs import nasnet_super_nets as nas_super_nets
+    return nas_super_nets[config.name](config.C, config.N, config.steps, config.multiplier, \
+                    config.stem_multiplier, config.num_classes, config.space, config.affine, config.track_running_stats)
+  elif config.name == 'infer.tiny':
+    from .cell_infers import TinyNetwork
+    if hasattr(config, 'genotype'):
+      genotype = config.genotype
+    elif hasattr(config, 'arch_str'):
+      genotype = CellStructure.str2structure(config.arch_str)
+    else: raise ValueError('Can not find genotype from this config : {:}'.format(config))
+    return TinyNetwork(config.C, config.N, genotype, config.num_classes)
+  elif config.name == 'infer.shape.tiny':
+    from .shape_infers import DynamicShapeTinyNet
+    if isinstance(config.channels, str):
+      channels = tuple([int(x) for x in config.channels.split(':')])
+    else: channels = config.channels
+    genotype = CellStructure.str2structure(config.genotype)
+    return DynamicShapeTinyNet(channels, genotype, config.num_classes)
+  elif config.name == 'infer.nasnet-cifar':
+    from .cell_infers import NASNetonCIFAR
+    raise NotImplementedError
+  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) -> List[Text]:
+  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))
+
+
+def get_cifar_models(config, extra_path=None):
+  super_type = getattr(config, 'super_type', 'basic')
+  if super_type == 'basic':
+    from .CifarResNet      import CifarResNet
+    from .CifarDenseNet    import DenseNet
+    from .CifarWideResNet  import CifarWideResNet
+    if config.arch == 'resnet':
+      return CifarResNet(config.module, config.depth, config.class_num, config.zero_init_residual)
+    elif config.arch == 'densenet':
+      return DenseNet(config.growthRate, config.depth, config.reduction, config.class_num, config.bottleneck)
+    elif config.arch == 'wideresnet':
+      return CifarWideResNet(config.depth, config.wide_factor, config.class_num, config.dropout)
+    else:
+      raise ValueError('invalid module type : {:}'.format(config.arch))
+  elif super_type.startswith('infer'):
+    from .shape_infers import InferWidthCifarResNet
+    from .shape_infers import InferDepthCifarResNet
+    from .shape_infers import InferCifarResNet
+    from .cell_infers import NASNetonCIFAR
+    assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
+    infer_mode = super_type.split('-')[1]
+    if infer_mode == 'width':
+      return InferWidthCifarResNet(config.module, config.depth, config.xchannels, config.class_num, config.zero_init_residual)
+    elif infer_mode == 'depth':
+      return InferDepthCifarResNet(config.module, config.depth, config.xblocks, config.class_num, config.zero_init_residual)
+    elif infer_mode == 'shape':
+      return InferCifarResNet(config.module, config.depth, config.xblocks, config.xchannels, config.class_num, config.zero_init_residual)
+    elif infer_mode == 'nasnet.cifar':
+      genotype = config.genotype
+      if extra_path is not None:  # reload genotype by extra_path
+        if not osp.isfile(extra_path): raise ValueError('invalid extra_path : {:}'.format(extra_path))
+        xdata = torch.load(extra_path)
+        current_epoch = xdata['epoch']
+        genotype = xdata['genotypes'][current_epoch-1]
+      C = config.C if hasattr(config, 'C') else config.ichannel
+      N = config.N if hasattr(config, 'N') else config.layers
+      return NASNetonCIFAR(C, N, config.stem_multi, config.class_num, genotype, config.auxiliary)
+    else:
+      raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
+  else:
+    raise ValueError('invalid super-type : {:}'.format(super_type))
+
+
+def get_imagenet_models(config):
+  super_type = getattr(config, 'super_type', 'basic')
+  if super_type == 'basic':
+    from .ImageNet_ResNet import ResNet
+    from .ImageNet_MobileNetV2 import MobileNetV2
+    if config.arch == 'resnet':
+      return ResNet(config.block_name, config.layers, config.deep_stem, config.class_num, config.zero_init_residual, config.groups, config.width_per_group)
+    elif config.arch == 'mobilenet_v2':
+      return MobileNetV2(config.class_num, config.width_multi, config.input_channel, config.last_channel, 'InvertedResidual', config.dropout)
+    else:
+      raise ValueError('invalid arch : {:}'.format( config.arch ))
+  elif super_type.startswith('infer'): # NAS searched architecture
+    assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
+    infer_mode = super_type.split('-')[1]
+    if infer_mode == 'shape':
+      from .shape_infers import InferImagenetResNet
+      from .shape_infers import InferMobileNetV2
+      if config.arch == 'resnet':
+        return InferImagenetResNet(config.block_name, config.layers, config.xblocks, config.xchannels, config.deep_stem, config.class_num, config.zero_init_residual)
+      elif config.arch == "MobileNetV2":
+        return InferMobileNetV2(config.class_num, config.xchannels, config.xblocks, config.dropout)
+      else:
+        raise ValueError('invalid arch-mode : {:}'.format(config.arch))
+    else:
+      raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
+  else:
+    raise ValueError('invalid super-type : {:}'.format(super_type))
+
+
+# Try to obtain the network by config.
+def obtain_model(config, extra_path=None):
+  if config.dataset == 'cifar':
+    return get_cifar_models(config, extra_path)
+  elif config.dataset == 'imagenet':
+    return get_imagenet_models(config)
+  else:
+    raise ValueError('invalid dataset in the model config : {:}'.format(config))
+
+
+def obtain_search_model(config):
+  if config.dataset == 'cifar':
+    if config.arch == 'resnet':
+      from .shape_searchs import SearchWidthCifarResNet
+      from .shape_searchs import SearchDepthCifarResNet
+      from .shape_searchs import SearchShapeCifarResNet
+      if config.search_mode == 'width':
+        return SearchWidthCifarResNet(config.module, config.depth, config.class_num)
+      elif config.search_mode == 'depth':
+        return SearchDepthCifarResNet(config.module, config.depth, config.class_num)
+      elif config.search_mode == 'shape':
+        return SearchShapeCifarResNet(config.module, config.depth, config.class_num)
+      else: raise ValueError('invalid search mode : {:}'.format(config.search_mode))
+    elif config.arch == 'simres':
+      from .shape_searchs import SearchWidthSimResNet
+      if config.search_mode == 'width':
+        return SearchWidthSimResNet(config.depth, config.class_num)
+      else: raise ValueError('invalid search mode : {:}'.format(config.search_mode))
+    else:
+      raise ValueError('invalid arch : {:} for dataset [{:}]'.format(config.arch, config.dataset))
+  elif config.dataset == 'imagenet':
+    from .shape_searchs import SearchShapeImagenetResNet
+    assert config.search_mode == 'shape', 'invalid search-mode : {:}'.format( config.search_mode )
+    if config.arch == 'resnet':
+      return SearchShapeImagenetResNet(config.block_name, config.layers, config.deep_stem, config.class_num)
+    else:
+      raise ValueError('invalid model config : {:}'.format(config))
+  else:
+    raise ValueError('invalid dataset in the model config : {:}'.format(config))
+
+
+def load_net_from_checkpoint(checkpoint):
+  assert osp.isfile(checkpoint), 'checkpoint {:} does not exist'.format(checkpoint)
+  checkpoint   = torch.load(checkpoint)
+  model_config = dict2config(checkpoint['model-config'], None)
+  model        = obtain_model(model_config)
+  model.load_state_dict(checkpoint['base-model'])
+  return model

models/cell_infers/__init__.py

@@ -0,0 +1,5 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from .tiny_network import TinyNetwork
+from .nasnet_cifar import NASNetonCIFAR

models/cell_infers/cells.py

@@ -0,0 +1,120 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from ..cell_operations import OPS
+
+
+# Cell for NAS-Bench-201
+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, True, True)
+        else:
+          layer = OPS[op_name](C_out, C_out,      1, True, True)
+        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]
+
+
+
+# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
+class NASNetInferCell(nn.Module):
+
+  def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev, affine, track_running_stats):
+    super(NASNetInferCell, self).__init__()
+    self.reduction = reduction
+    if reduction_prev: self.preprocess0 = OPS['skip_connect'](C_prev_prev, C, 2, affine, track_running_stats)
+    else             : self.preprocess0 = OPS['nor_conv_1x1'](C_prev_prev, C, 1, affine, track_running_stats)
+    self.preprocess1 = OPS['nor_conv_1x1'](C_prev, C, 1, affine, track_running_stats)
+
+    if not reduction:
+      nodes, concats = genotype['normal'], genotype['normal_concat']
+    else:
+      nodes, concats = genotype['reduce'], genotype['reduce_concat']
+    self._multiplier = len(concats)
+    self._concats = concats
+    self._steps = len(nodes)
+    self._nodes = nodes
+    self.edges = nn.ModuleDict()
+    for i, node in enumerate(nodes):
+      for in_node in node:
+        name, j = in_node[0], in_node[1]
+        stride = 2 if reduction and j < 2 else 1
+        node_str = '{:}<-{:}'.format(i+2, j)
+        self.edges[node_str] = OPS[name](C, C, stride, affine, track_running_stats)
+
+  # [TODO] to support drop_prob in this function..
+  def forward(self, s0, s1, unused_drop_prob):
+    s0 = self.preprocess0(s0)
+    s1 = self.preprocess1(s1)
+
+    states = [s0, s1]
+    for i, node in enumerate(self._nodes):
+      clist = []
+      for in_node in node:
+        name, j = in_node[0], in_node[1]
+        node_str = '{:}<-{:}'.format(i+2, j)
+        op = self.edges[ node_str ]
+        clist.append( op(states[j]) )
+      states.append( sum(clist) )
+    return torch.cat([states[x] for x in self._concats], dim=1)
+
+
+class AuxiliaryHeadCIFAR(nn.Module):
+
+  def __init__(self, C, num_classes):
+    """assuming input size 8x8"""
+    super(AuxiliaryHeadCIFAR, self).__init__()
+    self.features = nn.Sequential(
+      nn.ReLU(inplace=True),
+      nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False), # image size = 2 x 2
+      nn.Conv2d(C, 128, 1, bias=False),
+      nn.BatchNorm2d(128),
+      nn.ReLU(inplace=True),
+      nn.Conv2d(128, 768, 2, bias=False),
+      nn.BatchNorm2d(768),
+      nn.ReLU(inplace=True)
+    )
+    self.classifier = nn.Linear(768, num_classes)
+
+  def forward(self, x):
+    x = self.features(x)
+    x = self.classifier(x.view(x.size(0),-1))
+    return x

models/cell_infers/nasnet_cifar.py

@@ -0,0 +1,71 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR
+
+
+# The macro structure is based on NASNet
+class NASNetonCIFAR(nn.Module):
+
+  def __init__(self, C, N, stem_multiplier, num_classes, genotype, auxiliary, affine=True, track_running_stats=True):
+    super(NASNetonCIFAR, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(C*stem_multiplier))
+  
+    # config for each layer
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * (N-1) + [C*4 ] + [C*4  ] * (N-1)
+    layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
+
+    C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
+    self.auxiliary_index = None
+    self.auxiliary_head  = None
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell = InferCell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
+      self.cells.append( cell )
+      C_prev_prev, C_prev, reduction_prev = C_prev, cell._multiplier*C_curr, reduction
+      if reduction and C_curr == C*4 and auxiliary:
+        self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes)
+        self.auxiliary_index = index
+    self._Layer     = len(self.cells)
+    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.drop_path_prob = -1
+
+  def update_drop_path(self, drop_path_prob):
+    self.drop_path_prob = drop_path_prob
+
+  def auxiliary_param(self):
+    if self.auxiliary_head is None: return []
+    else: return list( self.auxiliary_head.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}, N={_layerN}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def forward(self, inputs):
+    stem_feature, logits_aux = self.stem(inputs), None
+    cell_results = [stem_feature, stem_feature]
+    for i, cell in enumerate(self.cells):
+      cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob)
+      cell_results.append( cell_feature )
+      if self.auxiliary_index is not None and i == self.auxiliary_index and self.training:
+        logits_aux = self.auxiliary_head( cell_results[-1] )
+    out = self.lastact(cell_results[-1])
+    out = self.global_pooling( out )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+    if logits_aux is None: return out, logits
+    else: return out, [logits, logits_aux]

models/cell_infers/tiny_network.py

@@ -0,0 +1,58 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+from ..cell_operations import ResNetBasicblock
+from .cells import InferCell
+
+
+# The macro structure for architectures in NAS-Bench-201
+class TinyNetwork(nn.Module):
+
+  def __init__(self, C, N, genotype, num_classes):
+    super(TinyNetwork, self).__init__()
+    self._C               = C
+    self._layerN          = N
+
+    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 = C
+    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, True)
+      else:
+        cell = InferCell(genotype, C_prev, C_curr, 1)
+      self.cells.append( cell )
+      C_prev = cell.out_dim
+    self._Layer= len(self.cells)
+
+    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)
+
+  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}, 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):
+      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

models/cell_operations.py

@@ -0,0 +1,297 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+import torch.nn as nn
+
+__all__ = ['OPS', 'ResNetBasicblock', 'SearchSpaceNames']
+
+OPS = {
+  'none'         : lambda C_in, C_out, stride, affine, track_running_stats: Zero(C_in, C_out, stride),
+  'avg_pool_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'avg', affine, track_running_stats),
+  'max_pool_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'max', affine, track_running_stats),
+  'nor_conv_7x7' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (7,7), (stride,stride), (3,3), (1,1), affine, track_running_stats),
+  'nor_conv_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
+  'nor_conv_1x1' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1), affine, track_running_stats),
+  'dua_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
+  'dua_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (5,5), (stride,stride), (2,2), (1,1), affine, track_running_stats),
+  'dil_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (3,3), (stride,stride), (2,2), (2,2), affine, track_running_stats),
+  'dil_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (5,5), (stride,stride), (4,4), (2,2), affine, track_running_stats),
+  'skip_connect' : lambda C_in, C_out, stride, affine, track_running_stats: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
+}
+
+CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3']
+NAS_BENCH_201         = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
+DARTS_SPACE           = ['none', 'skip_connect', 'dua_sepc_3x3', 'dua_sepc_5x5', 'dil_sepc_3x3', 'dil_sepc_5x5', 'avg_pool_3x3', 'max_pool_3x3']
+
+SearchSpaceNames = {'connect-nas'  : CONNECT_NAS_BENCHMARK,
+                    'nas-bench-201': NAS_BENCH_201,
+                    'darts'        : DARTS_SPACE}
+
+
+class ReLUConvBN(nn.Module):
+
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
+    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, affine=affine, track_running_stats=track_running_stats)
+    )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class SepConv(nn.Module):
+    
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
+    super(SepConv, self).__init__()
+    self.op = nn.Sequential(
+      nn.ReLU(inplace=False),
+      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
+      nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+      nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats),
+      )
+
+  def forward(self, x):
+    return self.op(x)
+
+
+class DualSepConv(nn.Module):
+    
+  def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
+    super(DualSepConv, self).__init__()
+    self.op_a = SepConv(C_in, C_in , kernel_size, stride, padding, dilation, affine, track_running_stats)
+    self.op_b = SepConv(C_in, C_out, kernel_size, 1, padding, dilation, affine, track_running_stats)
+
+  def forward(self, x):
+    x = self.op_a(x)
+    x = self.op_b(x)
+    return x
+
+
+class ResNetBasicblock(nn.Module):
+
+  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, 1, 1, affine)
+    self.conv_b = ReLUConvBN(  planes, planes, 3,      1, 1, 1, affine)
+    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, affine)
+    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
+
+
+class POOLING(nn.Module):
+
+  def __init__(self, C_in, C_out, stride, mode, affine=True, track_running_stats=True):
+    super(POOLING, self).__init__()
+    if C_in == C_out:
+      self.preprocess = None
+    else:
+      self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, 1, affine, track_running_stats)
+    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 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, affine, track_running_stats):
+    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)
+    elif stride == 1:
+      self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=False)
+    else:
+      raise ValueError('Invalid stride : {:}'.format(stride))
+    self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
+
+  def forward(self, x):
+    if self.stride == 2:
+      x = self.relu(x)
+      y = self.pad(x)
+      out = torch.cat([self.convs[0](x), self.convs[1](y[:,:,1:,1:])], dim=1)
+    else:
+      out = self.conv(x)
+    out = self.bn(out)
+    return out
+
+  def extra_repr(self):
+    return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)
+
+
+# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
+class PartAwareOp(nn.Module):
+
+  def __init__(self, C_in, C_out, stride, part=4):
+    super().__init__()
+    self.part   = 4
+    self.hidden = C_in // 3
+    self.avg_pool = nn.AdaptiveAvgPool2d(1)
+    self.local_conv_list = nn.ModuleList()
+    for i in range(self.part):
+      self.local_conv_list.append(
+            nn.Sequential(nn.ReLU(), nn.Conv2d(C_in, self.hidden, 1), nn.BatchNorm2d(self.hidden, affine=True))
+            )
+    self.W_K = nn.Linear(self.hidden, self.hidden)
+    self.W_Q = nn.Linear(self.hidden, self.hidden)
+
+    if stride == 2  : self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
+    elif stride == 1: self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
+    else:             raise ValueError('Invalid Stride : {:}'.format(stride))
+
+  def forward(self, x):
+    batch, C, H, W = x.size()
+    assert H >= self.part, 'input size too small : {:} vs {:}'.format(x.shape, self.part)
+    IHs = [0]
+    for i in range(self.part): IHs.append( min(H, int((i+1)*(float(H)/self.part))) )
+    local_feat_list = []
+    for i in range(self.part):
+      feature = x[:, :, IHs[i]:IHs[i+1], :]
+      xfeax   = self.avg_pool(feature)
+      xfea    = self.local_conv_list[i]( xfeax )
+      local_feat_list.append( xfea )
+    part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
+    part_feature = part_feature.transpose(1,2).contiguous()
+    part_K       = self.W_K(part_feature)
+    part_Q       = self.W_Q(part_feature).transpose(1,2).contiguous()
+    weight_att   = torch.bmm(part_K, part_Q)
+    attention    = torch.softmax(weight_att, dim=2)
+    aggreateF    = torch.bmm(attention, part_feature).transpose(1,2).contiguous()
+    features = []
+    for i in range(self.part):
+      feature = aggreateF[:, :, i:i+1].expand(batch, self.hidden, IHs[i+1]-IHs[i])
+      feature = feature.view(batch, self.hidden, IHs[i+1]-IHs[i], 1)
+      features.append( feature )
+    features  = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
+    final_fea = torch.cat((x,features), dim=1)
+    outputs   = self.last( final_fea )
+    return outputs
+
+
+# Searching for A Robust Neural Architecture in Four GPU Hours
+class GDAS_Reduction_Cell(nn.Module):
+
+  def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier, affine, track_running_stats):
+    super(GDAS_Reduction_Cell, self).__init__()
+    if reduction_prev:
+      self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
+    else:
+      self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
+    self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
+    self.multiplier  = multiplier
+
+    self.reduction = True
+    self.ops1 = nn.ModuleList(
+                  [nn.Sequential(
+                      nn.ReLU(inplace=False),
+                      nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
+                      nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
+                      nn.BatchNorm2d(C, affine=True),
+                      nn.ReLU(inplace=False),
+                      nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
+                      nn.BatchNorm2d(C, affine=True)),
+                   nn.Sequential(
+                      nn.ReLU(inplace=False),
+                      nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
+                      nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
+                      nn.BatchNorm2d(C, affine=True),
+                      nn.ReLU(inplace=False),
+                      nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
+                      nn.BatchNorm2d(C, affine=True))])
+
+    self.ops2 = nn.ModuleList(
+                  [nn.Sequential(
+                      nn.MaxPool2d(3, stride=1, padding=1),
+                      nn.BatchNorm2d(C, affine=True)),
+                   nn.Sequential(
+                      nn.MaxPool2d(3, stride=2, padding=1),
+                      nn.BatchNorm2d(C, affine=True))])
+
+  def forward(self, s0, s1, drop_prob = -1):
+    s0 = self.preprocess0(s0)
+    s1 = self.preprocess1(s1)
+
+    X0 = self.ops1[0] (s0)
+    X1 = self.ops1[1] (s1)
+    if self.training and drop_prob > 0.:
+      X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
+
+    #X2 = self.ops2[0] (X0+X1)
+    X2 = self.ops2[0] (s0)
+    X3 = self.ops2[1] (s1)
+    if self.training and drop_prob > 0.:
+      X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
+    return torch.cat([X0, X1, X2, X3], dim=1)

models/cell_searchs/__init__.py

@@ -0,0 +1,24 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+# The macro structure is defined in NAS-Bench-201
+from .search_model_darts    import TinyNetworkDarts
+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
+# NASNet-based macro structure
+from .search_model_gdas_nasnet import NASNetworkGDAS
+from .search_model_darts_nasnet import NASNetworkDARTS
+
+
+nas201_super_nets = {'DARTS-V1': TinyNetworkDarts,
+                     "DARTS-V2": TinyNetworkDarts,
+                     "GDAS": TinyNetworkGDAS,
+                     "SETN": TinyNetworkSETN,
+                     "ENAS": TinyNetworkENAS,
+                     "RANDOM": TinyNetworkRANDOM}
+
+nasnet_super_nets = {"GDAS": NASNetworkGDAS,
+                     "DARTS": NASNetworkDARTS}

models/cell_searchs/_test_module.py

@@ -0,0 +1,12 @@
+##################################################
+# 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()

models/cell_searchs/genotypes.py

@@ -0,0 +1,199 @@
+##################################################
+# 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] is 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 is None:
+          x = '('+nodes[xin]+')' + '@{:}'.format(op)
+        elif 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}

models/cell_searchs/search_cells.py

@@ -0,0 +1,197 @@
+##################################################
+# 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
+
+
+# This module is used for NAS-Bench-201, represents a small search space with a complete DAG
+class NAS201SearchCell(nn.Module):
+
+  def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False, track_running_stats=True):
+    super(NAS201SearchCell, 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, affine, track_running_stats) for op_name in op_names]
+        else:
+          xlists = [OPS[op_name](C_in , C_out,      1, affine, track_running_stats) 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_gdas(self, inputs, hardwts, index):
+    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) )
+    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, SETN
+  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 is 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]
+
+
+
+class MixedOp(nn.Module):
+
+  def __init__(self, space, C, stride, affine, track_running_stats):
+    super(MixedOp, self).__init__()
+    self._ops = nn.ModuleList()
+    for primitive in space:
+      op = OPS[primitive](C, C, stride, affine, track_running_stats)
+      self._ops.append(op)
+
+  def forward_gdas(self, x, weights, index):
+    return self._ops[index](x) * weights[index]
+
+  def forward_darts(self, x, weights):
+    return sum(w * op(x) for w, op in zip(weights, self._ops))
+
+
+# Learning Transferable Architectures for Scalable Image Recognition, CVPR 2018
+class NASNetSearchCell(nn.Module):
+
+  def __init__(self, space, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev, affine, track_running_stats):
+    super(NASNetSearchCell, self).__init__()
+    self.reduction = reduction
+    self.op_names  = deepcopy(space)
+    if reduction_prev: self.preprocess0 = OPS['skip_connect'](C_prev_prev, C, 2, affine, track_running_stats)
+    else             : self.preprocess0 = OPS['nor_conv_1x1'](C_prev_prev, C, 1, affine, track_running_stats)
+    self.preprocess1 = OPS['nor_conv_1x1'](C_prev, C, 1, affine, track_running_stats)
+    self._steps = steps
+    self._multiplier = multiplier
+
+    self._ops = nn.ModuleList()
+    self.edges     = nn.ModuleDict()
+    for i in range(self._steps):
+      for j in range(2+i):
+        node_str = '{:}<-{:}'.format(i, j)  # indicate the edge from node-(j) to node-(i+2)
+        stride = 2 if reduction and j < 2 else 1
+        op = MixedOp(space, C, stride, affine, track_running_stats)
+        self.edges[ node_str ] = op
+    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 forward_gdas(self, s0, s1, weightss, indexs):
+    s0 = self.preprocess0(s0)
+    s1 = self.preprocess1(s1)
+
+    states = [s0, s1]
+    for i in range(self._steps):
+      clist = []
+      for j, h in enumerate(states):
+        node_str = '{:}<-{:}'.format(i, j)
+        op = self.edges[ node_str ]
+        weights = weightss[ self.edge2index[node_str] ]
+        index   = indexs[ self.edge2index[node_str] ].item()
+        clist.append( op.forward_gdas(h, weights, index) )
+      states.append( sum(clist) )
+
+    return torch.cat(states[-self._multiplier:], dim=1)
+
+  def forward_darts(self, s0, s1, weightss):
+    s0 = self.preprocess0(s0)
+    s1 = self.preprocess1(s1)
+
+    states = [s0, s1]
+    for i in range(self._steps):
+      clist = []
+      for j, h in enumerate(states):
+        node_str = '{:}<-{:}'.format(i, j)
+        op = self.edges[ node_str ]
+        weights = weightss[ self.edge2index[node_str] ]
+        clist.append( op.forward_darts(h, weights) )
+      states.append( sum(clist) )
+
+    return torch.cat(states[-self._multiplier:], dim=1)

models/cell_searchs/search_model_darts.py

@@ -0,0 +1,97 @@
+##################################################
+# 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 NAS201SearchCell as SearchCell
+from .genotypes        import Structure
+
+
+class TinyNetworkDarts(nn.Module):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
+    super(TinyNetworkDarts, 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, affine, track_running_stats)
+        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 show_alphas(self):
+    with torch.no_grad():
+      return 'arch-parameters :\n{:}'.format( nn.functional.softmax(self.arch_parameters, dim=-1).cpu() )
+
+  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

models/cell_searchs/search_model_darts_nasnet.py

@@ -0,0 +1,108 @@
+####################
+# DARTS, ICLR 2019 #
+####################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from typing import List, Text, Dict
+from .search_cells import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkDARTS(nn.Module):
+
+  def __init__(self, C: int, N: int, steps: int, multiplier: int, stem_multiplier: int,
+               num_classes: int, search_space: List[Text], affine: bool, track_running_stats: bool):
+    super(NASNetworkDARTS, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self._steps    = steps
+    self._multiplier = multiplier
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(C*stem_multiplier))
+  
+    # config for each layer
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * (N-1) + [C*4 ] + [C*4  ] * (N-1)
+    layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
+
+    num_edge, edge2index = None, None
+    C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
+
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
+      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_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
+    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_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    self.arch_reduce_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+
+  def get_weights(self) -> List[torch.nn.Parameter]:
+    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) -> List[torch.nn.Parameter]:
+    return [self.arch_normal_parameters, self.arch_reduce_parameters]
+
+  def show_alphas(self) -> Text:
+    with torch.no_grad():
+      A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
+      B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
+    return '{:}\n{:}'.format(A, B)
+
+  def get_message(self) -> Text:
+    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) -> Text:
+    return ('{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def genotype(self) -> Dict[Text, List]:
+    def _parse(weights):
+      gene = []
+      for i in range(self._steps):
+        edges = []
+        for j in range(2+i):
+          node_str = '{:}<-{:}'.format(i, j)
+          ws = weights[ self.edge2index[node_str] ]
+          for k, op_name in enumerate(self.op_names):
+            if op_name == 'none': continue
+            edges.append( (op_name, j, ws[k]) )
+        edges = sorted(edges, key=lambda x: -x[-1])
+        selected_edges = edges[:2]
+        gene.append( tuple(selected_edges) )
+      return gene
+    with torch.no_grad():
+      gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
+      gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
+    return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
+            'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
+
+  def forward(self, inputs):
+
+    normal_w = nn.functional.softmax(self.arch_normal_parameters, dim=1)
+    reduce_w = nn.functional.softmax(self.arch_reduce_parameters, dim=1)
+
+    s0 = s1 = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      if cell.reduction: ww = reduce_w
+      else             : ww = normal_w
+      s0, s1 = s1, cell.forward_darts(s0, s1, ww)
+    out = self.lastact(s1)
+    out = self.global_pooling( out )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+
+    return out, logits

models/cell_searchs/search_model_enas.py

@@ -0,0 +1,94 @@
+##################################################
+# 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 NAS201SearchCell as 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, affine, track_running_stats):
+    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, affine, track_running_stats)
+        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

models/cell_searchs/search_model_enas_utils.py

@@ -0,0 +1,55 @@
+##################################################
+# 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

models/cell_searchs/search_model_gdas.py

@@ -0,0 +1,111 @@
+###########################################################################
+# 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 NAS201SearchCell as SearchCell
+from .genotypes        import Structure
+
+
+class TinyNetworkGDAS(nn.Module):
+
+  #def __init__(self, C, N, max_nodes, num_classes, search_space, affine=False, track_running_stats=True):
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
+    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, affine, track_running_stats)
+        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 show_alphas(self):
+    with torch.no_grad():
+      return 'arch-parameters :\n{:}'.format( nn.functional.softmax(self.arch_parameters, dim=-1).cpu() )
+
+  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):
+    while True:
+      gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
+      logits  = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.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
+      else: break
+
+    feature = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      if isinstance(cell, SearchCell):
+        feature = cell.forward_gdas(feature, hardwts, index)
+      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

models/cell_searchs/search_model_gdas_nasnet.py

@@ -0,0 +1,125 @@
+###########################################################################
+# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
+###########################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from .search_cells import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkGDAS(nn.Module):
+
+  def __init__(self, C, N, steps, multiplier, stem_multiplier, num_classes, search_space, affine, track_running_stats):
+    super(NASNetworkGDAS, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self._steps    = steps
+    self._multiplier = multiplier
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(C*stem_multiplier))
+  
+    # config for each layer
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * (N-1) + [C*4 ] + [C*4  ] * (N-1)
+    layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
+
+    num_edge, edge2index = None, None
+    C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
+
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
+      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_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
+    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_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    self.arch_reduce_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_normal_parameters, self.arch_reduce_parameters]
+
+  def show_alphas(self):
+    with torch.no_grad():
+      A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
+      B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
+    return '{:}\n{:}'.format(A, B)
+
+  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}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def genotype(self):
+    def _parse(weights):
+      gene = []
+      for i in range(self._steps):
+        edges = []
+        for j in range(2+i):
+          node_str = '{:}<-{:}'.format(i, j)
+          ws = weights[ self.edge2index[node_str] ]
+          for k, op_name in enumerate(self.op_names):
+            if op_name == 'none': continue
+            edges.append( (op_name, j, ws[k]) )
+        edges = sorted(edges, key=lambda x: -x[-1])
+        selected_edges = edges[:2]
+        gene.append( tuple(selected_edges) )
+      return gene
+    with torch.no_grad():
+      gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
+      gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
+    return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
+            'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
+
+  def forward(self, inputs):
+    def get_gumbel_prob(xins):
+      while True:
+        gumbels = -torch.empty_like(xins).exponential_().log()
+        logits  = (xins.log_softmax(dim=1) + gumbels) / self.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
+        else: break
+      return hardwts, index
+
+    normal_hardwts, normal_index = get_gumbel_prob(self.arch_normal_parameters)
+    reduce_hardwts, reduce_index = get_gumbel_prob(self.arch_reduce_parameters)
+
+    s0 = s1 = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      if cell.reduction: hardwts, index = reduce_hardwts, reduce_index
+      else             : hardwts, index = normal_hardwts, normal_index
+      s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
+    out = self.lastact(s1)
+    out = self.global_pooling( out )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+
+    return out, logits

models/cell_searchs/search_model_random.py

@@ -0,0 +1,81 @@
+##################################################
+# 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 NAS201SearchCell as SearchCell
+from .genotypes        import Structure
+
+
+class TinyNetworkRANDOM(nn.Module):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
+    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, affine, track_running_stats)
+        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

models/cell_searchs/search_model_setn.py

@@ -0,0 +1,152 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+######################################################################################
+# 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 NAS201SearchCell as SearchCell
+from .genotypes        import Structure
+
+
+class TinyNetworkSETN(nn.Module):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
+    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, affine, track_running_stats)
+        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

models/cell_searchs/search_model_setn_nasnet.py

@@ -0,0 +1,139 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+######################################################################################
+# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
+######################################################################################
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from typing import List, Text, Dict
+from .search_cells     import NASNetSearchCell as SearchCell
+
+
+# The macro structure is based on NASNet
+class NASNetworkSETN(nn.Module):
+
+  def __init__(self, C: int, N: int, steps: int, multiplier: int, stem_multiplier: int,
+               num_classes: int, search_space: List[Text], affine: bool, track_running_stats: bool):
+    super(NASNetworkSETN, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self._steps    = steps
+    self._multiplier = multiplier
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, C*stem_multiplier, kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(C*stem_multiplier))
+  
+    # config for each layer
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * (N-1) + [C*4 ] + [C*4  ] * (N-1)
+    layer_reductions = [False] * N + [True] + [False] * (N-1) + [True] + [False] * (N-1)
+
+    num_edge, edge2index = None, None
+    C_prev_prev, C_prev, C_curr, reduction_prev = C*stem_multiplier, C*stem_multiplier, C, False
+
+    self.cells = nn.ModuleList()
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine, track_running_stats)
+      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_prev, C_prev, reduction_prev = C_prev, multiplier*C_curr, reduction
+    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_normal_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    self.arch_reduce_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_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_normal_parameters, self.arch_reduce_parameters]
+
+  def show_alphas(self):
+    with torch.no_grad():
+      A = 'arch-normal-parameters :\n{:}'.format( nn.functional.softmax(self.arch_normal_parameters, dim=-1).cpu() )
+      B = 'arch-reduce-parameters :\n{:}'.format( nn.functional.softmax(self.arch_reduce_parameters, dim=-1).cpu() )
+    return '{:}\n{:}'.format(A, B)
+
+  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}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  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 genotype(self):
+    def _parse(weights):
+      gene = []
+      for i in range(self._steps):
+        edges = []
+        for j in range(2+i):
+          node_str = '{:}<-{:}'.format(i, j)
+          ws = weights[ self.edge2index[node_str] ]
+          for k, op_name in enumerate(self.op_names):
+            if op_name == 'none': continue
+            edges.append( (op_name, j, ws[k]) )
+        edges = sorted(edges, key=lambda x: -x[-1])
+        selected_edges = edges[:2]
+        gene.append( tuple(selected_edges) )
+      return gene
+    with torch.no_grad():
+      gene_normal = _parse(torch.softmax(self.arch_normal_parameters, dim=-1).cpu().numpy())
+      gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
+    return {'normal': gene_normal, 'normal_concat': list(range(2+self._steps-self._multiplier, self._steps+2)),
+            'reduce': gene_reduce, 'reduce_concat': list(range(2+self._steps-self._multiplier, self._steps+2))}
+
+  def forward(self, inputs):
+    normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
+    reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)
+
+    s0 = s1 = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      # [TODO]
+      raise NotImplementedError
+      if cell.reduction: hardwts, index = reduce_hardwts, reduce_index
+      else             : hardwts, index = normal_hardwts, normal_index
+      s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
+    out = self.lastact(s1)
+    out = self.global_pooling( out )
+    out = out.view(out.size(0), -1)
+    logits = self.classifier(out)
+
+    return out, logits

models/clone_weights.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+
+
+def copy_conv(module, init):
+  assert isinstance(module, nn.Conv2d), 'invalid module : {:}'.format(module)
+  assert isinstance(init  , nn.Conv2d), 'invalid module : {:}'.format(init)
+  new_i, new_o = module.in_channels, module.out_channels
+  module.weight.copy_( init.weight.detach()[:new_o, :new_i] )
+  if module.bias is not None:
+    module.bias.copy_( init.bias.detach()[:new_o] )
+
+def copy_bn  (module, init):
+  assert isinstance(module, nn.BatchNorm2d), 'invalid module : {:}'.format(module)
+  assert isinstance(init  , nn.BatchNorm2d), 'invalid module : {:}'.format(init)
+  num_features = module.num_features
+  if module.weight is not None:
+    module.weight.copy_( init.weight.detach()[:num_features] )
+  if module.bias is not None:
+    module.bias.copy_( init.bias.detach()[:num_features] )
+  if module.running_mean is not None:
+    module.running_mean.copy_( init.running_mean.detach()[:num_features] )
+  if module.running_var  is not None:
+    module.running_var.copy_( init.running_var.detach()[:num_features] )
+
+def copy_fc  (module, init):
+  assert isinstance(module, nn.Linear), 'invalid module : {:}'.format(module)
+  assert isinstance(init  , nn.Linear), 'invalid module : {:}'.format(init)
+  new_i, new_o = module.in_features, module.out_features
+  module.weight.copy_( init.weight.detach()[:new_o, :new_i] )
+  if module.bias is not None:
+    module.bias.copy_( init.bias.detach()[:new_o] )
+
+def copy_base(module, init):
+  assert type(module).__name__ in ['ConvBNReLU', 'Downsample'], 'invalid module : {:}'.format(module)
+  assert type(  init).__name__ in ['ConvBNReLU', 'Downsample'], 'invalid module : {:}'.format(  init)
+  if module.conv is not None:
+    copy_conv(module.conv, init.conv)
+  if module.bn is not None:
+    copy_bn  (module.bn, init.bn)
+
+def copy_basic(module, init):
+  copy_base(module.conv_a, init.conv_a)
+  copy_base(module.conv_b, init.conv_b)
+  if module.downsample is not None:
+    if init.downsample is not None:
+      copy_base(module.downsample, init.downsample)
+    #else:
+    # import pdb; pdb.set_trace()
+
+
+def init_from_model(network, init_model):
+  with torch.no_grad():
+    copy_fc(network.classifier, init_model.classifier)
+    for base, target in zip(init_model.layers, network.layers):
+      assert type(base).__name__  == type(target).__name__, 'invalid type : {:} vs {:}'.format(base, target)
+      if type(base).__name__ == 'ConvBNReLU':
+        copy_base(target, base)
+      elif type(base).__name__ == 'ResNetBasicblock':
+        copy_basic(target, base)
+      else:
+        raise ValueError('unknown type name : {:}'.format( type(base).__name__ ))

models/initialization.py

@@ -0,0 +1,18 @@
+import torch
+import torch.nn as nn
+
+
+def initialize_resnet(m):
+  if isinstance(m, nn.Conv2d):
+    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+    if m.bias is not None:
+      nn.init.constant_(m.bias, 0)
+  elif isinstance(m, nn.BatchNorm2d):
+    nn.init.constant_(m.weight, 1)
+    if m.bias is not None:
+      nn.init.constant_(m.bias, 0)
+  elif isinstance(m, nn.Linear):
+    nn.init.normal_(m.weight, 0, 0.01)
+    nn.init.constant_(m.bias, 0)
+
+

models/shape_infers/InferCifarResNet.py

@@ -0,0 +1,167 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+  
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    else       : self.bn  = None
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+
+  def forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.bn  : out = self.bn( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  num_conv  = 2
+  expansion = 1
+  def __init__(self, iCs, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
+    assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
+    
+    self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    residual_in = iCs[0]
+    if stride == 2:
+      self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+      residual_in = iCs[2]
+    elif iCs[0] != iCs[2]:
+      self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    #self.out_dim  = max(residual_in, iCs[2])
+    self.out_dim  = iCs[2]
+
+  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
+    out = residual + basicblock
+    return F.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, iCs, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
+    assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
+    self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    residual_in = iCs[0]
+    if stride == 2:
+      self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
+      residual_in     = iCs[3]
+    elif iCs[0] != iCs[3]:
+      self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
+      residual_in     = iCs[3]
+    else:
+      self.downsample = None
+    #self.out_dim = max(residual_in, iCs[3])
+    self.out_dim = iCs[3]
+
+  def forward(self, inputs):
+
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    out = residual + bottleneck
+    return F.relu(out, inplace=True)
+
+
+
+class InferCifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, xblocks, xchannels, num_classes, zero_init_residual):
+    super(InferCifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+    assert len(xblocks) == 3, 'invalid xblocks : {:}'.format(xblocks)
+
+    self.message     = 'InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes = num_classes
+    self.xchannels   = xchannels
+    self.layers      = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    last_channel_idx = 1
+    for stage in range(3):
+      for iL in range(layer_blocks):
+        num_conv = block.num_conv 
+        iCs      = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
+        stride   = 2 if stage > 0 and iL == 0 else 1
+        module   = block(iCs, stride)
+        last_channel_idx += num_conv
+        self.xchannels[last_channel_idx] = module.out_dim
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
+        if iL + 1 == xblocks[stage]: # reach the maximum depth
+          out_channel = module.out_dim
+          for iiL in range(iL+1, layer_blocks):
+            last_channel_idx += num_conv
+          self.xchannels[last_channel_idx] = module.out_dim
+          break
+  
+    self.avgpool    = nn.AvgPool2d(8)
+    self.classifier = nn.Linear(self.xchannels[-1], num_classes)
+    
+    self.apply(initialize_resnet)
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, ResNetBasicblock):
+          nn.init.constant_(m.conv_b.bn.weight, 0)
+        elif isinstance(m, ResNetBottleneck):
+          nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_infers/InferCifarResNet_depth.py

@@ -0,0 +1,150 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+  
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    else       : self.bn  = None
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+
+  def forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.bn  : out = self.bn( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  num_conv  = 2
+  expansion = 1
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    
+    self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(  planes, planes, 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim  = planes
+
+  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
+    out = residual + basicblock
+    return F.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_1x1 = ConvBNReLU(inplanes, planes, 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(  planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
+    elif inplanes != planes*self.expansion:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim = planes*self.expansion
+
+  def forward(self, inputs):
+
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    out = residual + bottleneck
+    return F.relu(out, inplace=True)
+
+
+
+class InferDepthCifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, xblocks, num_classes, zero_init_residual):
+    super(InferDepthCifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+    assert len(xblocks) == 3, 'invalid xblocks : {:}'.format(xblocks)
+
+    self.message     = 'InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes = num_classes
+    self.layers      = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    self.channels    = [16]
+    for stage in range(3):
+      for iL in range(layer_blocks):
+        iC       = self.channels[-1]
+        planes = 16 * (2**stage)
+        stride   = 2 if stage > 0 and iL == 0 else 1
+        module   = block(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, planes, module.out_dim, stride)
+        if iL + 1 == xblocks[stage]: # reach the maximum depth
+          break
+  
+    self.avgpool    = nn.AvgPool2d(8)
+    self.classifier = nn.Linear(self.channels[-1], num_classes)
+    
+    self.apply(initialize_resnet)
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, ResNetBasicblock):
+          nn.init.constant_(m.conv_b.bn.weight, 0)
+        elif isinstance(m, ResNetBottleneck):
+          nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_infers/InferCifarResNet_width.py

@@ -0,0 +1,160 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+  
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    else       : self.bn  = None
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+
+  def forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.bn  : out = self.bn( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  num_conv  = 2
+  expansion = 1
+  def __init__(self, iCs, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
+    assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
+    
+    self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    residual_in = iCs[0]
+    if stride == 2:
+      self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+      residual_in = iCs[2]
+    elif iCs[0] != iCs[2]:
+      self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    #self.out_dim  = max(residual_in, iCs[2])
+    self.out_dim  = iCs[2]
+
+  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
+    out = residual + basicblock
+    return F.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, iCs, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
+    assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
+    self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    residual_in = iCs[0]
+    if stride == 2:
+      self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=False, has_relu=False)
+      residual_in     = iCs[3]
+    elif iCs[0] != iCs[3]:
+      self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=False, has_relu=False)
+      residual_in     = iCs[3]
+    else:
+      self.downsample = None
+    #self.out_dim = max(residual_in, iCs[3])
+    self.out_dim = iCs[3]
+
+  def forward(self, inputs):
+
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    out = residual + bottleneck
+    return F.relu(out, inplace=True)
+
+
+
+class InferWidthCifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, xchannels, num_classes, zero_init_residual):
+    super(InferWidthCifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+
+    self.message     = 'InferWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes = num_classes
+    self.xchannels   = xchannels
+    self.layers      = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    last_channel_idx = 1
+    for stage in range(3):
+      for iL in range(layer_blocks):
+        num_conv = block.num_conv 
+        iCs      = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
+        stride   = 2 if stage > 0 and iL == 0 else 1
+        module   = block(iCs, stride)
+        last_channel_idx += num_conv
+        self.xchannels[last_channel_idx] = module.out_dim
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
+  
+    self.avgpool    = nn.AvgPool2d(8)
+    self.classifier = nn.Linear(self.xchannels[-1], num_classes)
+    
+    self.apply(initialize_resnet)
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, ResNetBasicblock):
+          nn.init.constant_(m.conv_b.bn.weight, 0)
+        elif isinstance(m, ResNetBottleneck):
+          nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_infers/InferImagenetResNet.py

@@ -0,0 +1,170 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import torch.nn as nn
+import torch.nn.functional as F
+from ..initialization import initialize_resnet
+
+
+class ConvBNReLU(nn.Module):
+  
+  num_conv  = 1
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    else       : self.bn  = None
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+
+  def forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.bn  : out = self.bn( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  num_conv  = 2
+  expansion = 1
+  def __init__(self, iCs, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
+    assert len(iCs) == 3,'invalid lengths of iCs : {:}'.format(iCs)
+    
+    self.conv_a = ConvBNReLU(iCs[0], iCs[1], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(iCs[1], iCs[2], 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    residual_in = iCs[0]
+    if stride == 2:
+      self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=True, has_bn=True, has_relu=False)
+      residual_in = iCs[2]
+    elif iCs[0] != iCs[2]:
+      self.downsample = ConvBNReLU(iCs[0], iCs[2], 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    #self.out_dim  = max(residual_in, iCs[2])
+    self.out_dim  = iCs[2]
+
+  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
+    out = residual + basicblock
+    return F.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, iCs, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    assert isinstance(iCs, tuple) or isinstance(iCs, list), 'invalid type of iCs : {:}'.format( iCs )
+    assert len(iCs) == 4,'invalid lengths of iCs : {:}'.format(iCs)
+    self.conv_1x1 = ConvBNReLU(iCs[0], iCs[1], 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(iCs[1], iCs[2], 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(iCs[2], iCs[3], 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    residual_in = iCs[0]
+    if stride == 2:
+      self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=True , has_bn=True, has_relu=False)
+      residual_in     = iCs[3]
+    elif iCs[0] != iCs[3]:
+      self.downsample = ConvBNReLU(iCs[0], iCs[3], 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+      residual_in     = iCs[3]
+    else:
+      self.downsample = None
+    #self.out_dim = max(residual_in, iCs[3])
+    self.out_dim = iCs[3]
+
+  def forward(self, inputs):
+
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+
+    if self.downsample is not None:
+      residual = self.downsample(inputs)
+    else:
+      residual = inputs
+    out = residual + bottleneck
+    return F.relu(out, inplace=True)
+
+
+
+class InferImagenetResNet(nn.Module):
+
+  def __init__(self, block_name, layers, xblocks, xchannels, deep_stem, num_classes, zero_init_residual):
+    super(InferImagenetResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'BasicBlock':
+      block = ResNetBasicblock
+    elif block_name == 'Bottleneck':
+      block = ResNetBottleneck
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+    assert len(xblocks) == len(layers), 'invalid layers : {:} vs xblocks : {:}'.format(layers, xblocks)
+
+    self.message     = 'InferImagenetResNet : Depth : {:} -> {:}, Layers for each block : {:}'.format(sum(layers)*block.num_conv, sum(xblocks)*block.num_conv, xblocks)
+    self.num_classes = num_classes
+    self.xchannels   = xchannels
+    if not deep_stem:
+      self.layers      = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 7, 2, 3, False, has_avg=False, has_bn=True, has_relu=True) ] )
+      last_channel_idx = 1
+    else:
+      self.layers      = nn.ModuleList( [ ConvBNReLU(xchannels[0], xchannels[1], 3, 2, 1, False, has_avg=False, has_bn=True, has_relu=True)
+                                         ,ConvBNReLU(xchannels[1], xchannels[2], 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+      last_channel_idx = 2
+    self.layers.append( nn.MaxPool2d(kernel_size=3, stride=2, padding=1) )
+    for stage, layer_blocks in enumerate(layers):
+      for iL in range(layer_blocks):
+        num_conv = block.num_conv 
+        iCs      = self.xchannels[last_channel_idx:last_channel_idx+num_conv+1]
+        stride   = 2 if stage > 0 and iL == 0 else 1
+        module   = block(iCs, stride)
+        last_channel_idx += num_conv
+        self.xchannels[last_channel_idx] = module.out_dim
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iCs={:}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iCs, module.out_dim, stride)
+        if iL + 1 == xblocks[stage]: # reach the maximum depth
+          out_channel = module.out_dim
+          for iiL in range(iL+1, layer_blocks):
+            last_channel_idx += num_conv
+          self.xchannels[last_channel_idx] = module.out_dim
+          break
+    assert last_channel_idx + 1 == len(self.xchannels), '{:} vs {:}'.format(last_channel_idx, len(self.xchannels))
+    self.avgpool    = nn.AdaptiveAvgPool2d((1,1))
+    self.classifier = nn.Linear(self.xchannels[-1], num_classes)
+    
+    self.apply(initialize_resnet)
+    if zero_init_residual:
+      for m in self.modules():
+        if isinstance(m, ResNetBasicblock):
+          nn.init.constant_(m.conv_b.bn.weight, 0)
+        elif isinstance(m, ResNetBottleneck):
+          nn.init.constant_(m.conv_1x4.bn.weight, 0)
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_infers/InferMobileNetV2.py

@@ -0,0 +1,122 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+# MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018
+from torch import nn
+from ..initialization import initialize_resnet
+from ..SharedUtils    import parse_channel_info
+
+
+class ConvBNReLU(nn.Module):
+  def __init__(self, in_planes, out_planes, kernel_size, stride, groups, has_bn=True, has_relu=True):
+    super(ConvBNReLU, self).__init__()
+    padding = (kernel_size - 1) // 2
+    self.conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False)
+    if has_bn: self.bn = nn.BatchNorm2d(out_planes)
+    else     : self.bn = None
+    if has_relu: self.relu = nn.ReLU6(inplace=True)
+    else       : self.relu = None
+  
+  def forward(self, x):
+    out = self.conv( x )
+    if self.bn:   out = self.bn  ( out )
+    if self.relu: out = self.relu( out )
+    return out
+
+
+class InvertedResidual(nn.Module):
+  def __init__(self, channels, stride, expand_ratio, additive):
+    super(InvertedResidual, self).__init__()
+    self.stride = stride
+    assert stride in [1, 2], 'invalid stride : {:}'.format(stride)
+    assert len(channels) in [2, 3], 'invalid channels : {:}'.format(channels)
+
+    if len(channels) == 2:
+      layers = []
+    else:
+      layers = [ConvBNReLU(channels[0], channels[1], 1, 1, 1)]
+    layers.extend([
+      # dw
+      ConvBNReLU(channels[-2], channels[-2], 3, stride, channels[-2]),
+      # pw-linear
+      ConvBNReLU(channels[-2], channels[-1], 1, 1, 1, True, False),
+    ])
+    self.conv = nn.Sequential(*layers)
+    self.additive = additive
+    if self.additive and channels[0] != channels[-1]:
+      self.shortcut = ConvBNReLU(channels[0], channels[-1], 1, 1, 1, True, False)
+    else:
+      self.shortcut = None
+    self.out_dim  = channels[-1]
+
+  def forward(self, x):
+    out = self.conv(x)
+    # if self.additive: return additive_func(out, x)
+    if self.shortcut: return out + self.shortcut(x)
+    else            : return out
+
+
+class InferMobileNetV2(nn.Module):
+  def __init__(self, num_classes, xchannels, xblocks, dropout):
+    super(InferMobileNetV2, self).__init__()
+    block = InvertedResidual
+    inverted_residual_setting = [
+      # t, c,  n, s
+      [1, 16 , 1, 1],
+      [6, 24 , 2, 2],
+      [6, 32 , 3, 2],
+      [6, 64 , 4, 2],
+      [6, 96 , 3, 1],
+      [6, 160, 3, 2],
+      [6, 320, 1, 1],
+    ]
+    assert len(inverted_residual_setting) == len(xblocks), 'invalid number of layers : {:} vs {:}'.format(len(inverted_residual_setting), len(xblocks))
+    for block_num, ir_setting in zip(xblocks, inverted_residual_setting):
+      assert block_num <= ir_setting[2], '{:} vs {:}'.format(block_num, ir_setting)
+    xchannels = parse_channel_info(xchannels)
+    #for i, chs in enumerate(xchannels):
+    #  if i > 0: assert chs[0] == xchannels[i-1][-1], 'Layer[{:}] is invalid {:} vs {:}'.format(i, xchannels[i-1], chs)
+    self.xchannels = xchannels
+    self.message     = 'InferMobileNetV2 : xblocks={:}'.format(xblocks)
+    # building first layer
+    features = [ConvBNReLU(xchannels[0][0], xchannels[0][1], 3, 2, 1)]
+    last_channel_idx = 1
+
+    # building inverted residual blocks
+    for stage, (t, c, n, s) in enumerate(inverted_residual_setting):
+      for i in range(n):
+        stride = s if i == 0 else 1
+        additv = True if i > 0 else False
+        module = block(self.xchannels[last_channel_idx], stride, t, additv)
+        features.append(module)
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, Cs={:}, stride={:}, expand={:}, original-C={:}".format(stage, i, n, len(features), self.xchannels[last_channel_idx], stride, t, c)
+        last_channel_idx += 1
+        if i + 1 == xblocks[stage]:
+          out_channel = module.out_dim
+          for iiL in range(i+1, n):
+            last_channel_idx += 1
+          self.xchannels[last_channel_idx][0] = module.out_dim
+          break
+    # building last several layers
+    features.append(ConvBNReLU(self.xchannels[last_channel_idx][0], self.xchannels[last_channel_idx][1], 1, 1, 1))
+    assert last_channel_idx + 2 == len(self.xchannels), '{:} vs {:}'.format(last_channel_idx, len(self.xchannels))
+    # make it nn.Sequential
+    self.features = nn.Sequential(*features)
+
+    # building classifier
+    self.classifier = nn.Sequential(
+      nn.Dropout(dropout),
+      nn.Linear(self.xchannels[last_channel_idx][1], num_classes),
+    )
+
+    # weight initialization
+    self.apply( initialize_resnet )
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    features = self.features(inputs)
+    vectors  = features.mean([2, 3])
+    predicts = self.classifier(vectors)
+    return features, predicts

models/shape_infers/InferTinyCellNet.py

@@ -0,0 +1,58 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from typing import List, Text, Any
+import torch.nn as nn
+from models.cell_operations import ResNetBasicblock
+from models.cell_infers.cells import InferCell
+
+
+class DynamicShapeTinyNet(nn.Module):
+
+  def __init__(self, channels: List[int], genotype: Any, num_classes: int):
+    super(DynamicShapeTinyNet, self).__init__()
+    self._channels = channels
+    if len(channels) % 3 != 2:
+      raise ValueError('invalid number of layers : {:}'.format(len(channels)))
+    self._num_stage = N = len(channels) // 3
+
+    self.stem = nn.Sequential(
+                    nn.Conv2d(3, channels[0], kernel_size=3, padding=1, bias=False),
+                    nn.BatchNorm2d(channels[0]))
+
+    # 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 = channels[0]
+    self.cells = nn.ModuleList()
+    for index, (c_curr, reduction) in enumerate(zip(channels, layer_reductions)):
+      if reduction : cell = ResNetBasicblock(c_prev, c_curr, 2, True)
+      else         : cell = InferCell(genotype, c_prev, c_curr, 1)
+      self.cells.append( cell )
+      c_prev = cell.out_dim
+    self._num_layer = len(self.cells)
+
+    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)
+
+  def get_message(self) -> Text:
+    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={_channels}, N={_num_stage}, L={_num_layer})'.format(name=self.__class__.__name__, **self.__dict__))
+
+  def forward(self, inputs):
+    feature = self.stem(inputs)
+    for i, cell in enumerate(self.cells):
+      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

models/shape_infers/__init__.py

@@ -0,0 +1,9 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+from .InferCifarResNet_width import InferWidthCifarResNet
+from .InferImagenetResNet import InferImagenetResNet
+from .InferCifarResNet_depth import InferDepthCifarResNet
+from .InferCifarResNet import InferCifarResNet
+from .InferMobileNetV2 import InferMobileNetV2
+from .InferTinyCellNet import DynamicShapeTinyNet

models/shape_infers/shared_utils.py

@@ -0,0 +1,5 @@
+def parse_channel_info(xstring):
+  blocks = xstring.split(' ')
+  blocks = [x.split('-') for x in blocks]
+  blocks = [[int(_) for _ in x] for x in blocks]
+  return blocks

models/shape_searchs/SearchCifarResNet.py

@@ -0,0 +1,502 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+from collections import OrderedDict
+from bisect import bisect_right
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils    import additive_func
+from .SoftSelect      import select2withP, ChannelWiseInter
+from .SoftSelect      import linear_forward
+from .SoftSelect      import get_width_choices
+
+
+def get_depth_choices(nDepth, return_num):
+  if nDepth == 2:
+    choices = (1, 2)
+  elif nDepth == 3:
+    choices = (1, 2, 3)
+  elif nDepth > 3:
+    choices = list(range(1, nDepth+1, 2))
+    if choices[-1] < nDepth: choices.append(nDepth)
+  else:
+    raise ValueError('invalid nDepth : {:}'.format(nDepth))
+  if return_num: return len(choices)
+  else         : return choices
+  
+
+def conv_forward(inputs, conv, choices):
+  iC = conv.in_channels
+  fill_size = list(inputs.size())
+  fill_size[1] = iC - fill_size[1]
+  filled  = torch.zeros(fill_size, device=inputs.device)
+  xinputs = torch.cat((inputs, filled), dim=1)
+  outputs = conv(xinputs)
+  selecteds = [outputs[:,:oC] for oC in choices]
+  return selecteds
+
+
+class ConvBNReLU(nn.Module):
+  num_conv  = 1
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    self.InShape  = None
+    self.OutShape = None
+    self.choices  = get_width_choices(nOut)
+    self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
+
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    #if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    #else       : self.bn  = None
+    self.has_bn = has_bn
+    self.BNs  = nn.ModuleList()
+    for i, _out in enumerate(self.choices):
+      self.BNs.append(nn.BatchNorm2d(_out))
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+    self.in_dim   = nIn
+    self.out_dim  = nOut
+    self.search_mode = 'basic'
+
+  def get_flops(self, channels, check_range=True, divide=1):
+    iC, oC = channels
+    if check_range: assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:}  |  {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
+    assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
+    assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
+    #conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+    conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
+    all_positions = self.OutShape[0] * self.OutShape[1]
+    flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+    if self.conv.bias is not None: flops += all_positions / divide
+    return flops
+
+  def get_range(self):
+    return [self.choices]
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, index, prob = tuple_inputs
+    index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+    probability = torch.squeeze(probability)
+    assert len(index) == 2, 'invalid length : {:}'.format(index)
+    # compute expected flop
+    #coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+    expected_outC = (self.choices_tensor * probability).sum()
+    expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    # convolutional layer
+    out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+    out_bns   = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+    # merge
+    out_channel = max([x.size(1) for x in out_bns])
+    outA = ChannelWiseInter(out_bns[0], out_channel)
+    outB = ChannelWiseInter(out_bns[1], out_channel)
+    out  = outA * prob[0] + outB * prob[1]
+    #out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    return out, expected_outC, expected_flop
+
+  def basic_forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.has_bn:out= self.BNs[-1]( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    if self.InShape is None:
+      self.InShape  = (inputs.size(-2), inputs.size(-1))
+      self.OutShape = (out.size(-2)   , out.size(-1))
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  expansion = 1
+  num_conv  = 2
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(  planes, planes, 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_a.get_range() + self.conv_b.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 3, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv_a.get_flops([channels[0], channels[1]])
+    flop_B = self.conv_b.get_flops([channels[1], channels[2]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_C = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_C = channels[0] * channels[-1] * self.conv_b.OutShape[0] * self.conv_b.OutShape[1]
+    return flop_A + flop_B + flop_C
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
+    out_a, expected_inC_a, expected_flop_a = self.conv_a( (inputs, expected_inC  , probability[0], indexes[0], probs[0]) )
+    out_b, expected_inC_b, expected_flop_b = self.conv_b( (out_a , expected_inC_a, probability[1], indexes[1], probs[1]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[1], indexes[1], probs[1]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out_b)
+    return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
+
+  def basic_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
+    out = additive_func(residual, basicblock)
+    return nn.functional.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_1x1 = ConvBNReLU(inplanes, planes, 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(  planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes*self.expansion:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes * self.expansion
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_1x1.get_range() + self.conv_3x3.get_range() + self.conv_1x4.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 4, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
+    flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
+    flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_D = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_D = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_D = channels[0] * channels[-1] * self.conv_1x4.OutShape[0] * self.conv_1x4.OutShape[1]
+    return flop_A + flop_B + flop_C + flop_D
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def basic_forward(self, inputs):
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+    if self.downsample is not None: residual = self.downsample(inputs)
+    else                          : residual = inputs
+    out = additive_func(residual, bottleneck)
+    return nn.functional.relu(out, inplace=True)
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
+    out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( (inputs, expected_inC    , probability[0], indexes[0], probs[0]) )
+    out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( (out_1x1,expected_inC_1x1, probability[1], indexes[1], probs[1]) )
+    out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( (out_3x3,expected_inC_3x3, probability[2], indexes[2], probs[2]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[2], indexes[2], probs[2]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out_1x4)
+    return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
+
+
+class SearchShapeCifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, num_classes):
+    super(SearchShapeCifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+
+    self.message      = 'SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes  = num_classes
+    self.channels     = [16]
+    self.layers       = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    self.InShape      = None
+    self.depth_info   = OrderedDict()
+    self.depth_at_i   = OrderedDict()
+    for stage in range(3):
+      cur_block_choices = get_depth_choices(layer_blocks, False)
+      assert cur_block_choices[-1] == layer_blocks, 'stage={:}, {:} vs {:}'.format(stage, cur_block_choices, layer_blocks)
+      self.message += "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format(stage, cur_block_choices, layer_blocks)
+      block_choices, xstart = [], len(self.layers)
+      for iL in range(layer_blocks):
+        iC     = self.channels[-1]
+        planes = 16 * (2**stage)
+        stride = 2 if stage > 0 and iL == 0 else 1
+        module = block(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
+        # added for depth
+        layer_index = len(self.layers) - 1
+        if iL + 1 in cur_block_choices: block_choices.append( layer_index )
+        if iL + 1 == layer_blocks:
+          self.depth_info[layer_index] = {'choices': block_choices,
+                                          'stage'  : stage,
+                                          'xstart' : xstart}
+    self.depth_info_list = []
+    for xend, info in self.depth_info.items():
+      self.depth_info_list.append( (xend, info) )
+      xstart, xstage = info['xstart'], info['stage']
+      for ilayer in range(xstart, xend+1):
+        idx = bisect_right(info['choices'], ilayer-1)
+        self.depth_at_i[ilayer] = (xstage, idx)
+
+    self.avgpool     = nn.AvgPool2d(8)
+    self.classifier  = nn.Linear(module.out_dim, num_classes)
+    self.InShape     = None
+    self.tau         = -1
+    self.search_mode = 'basic'
+    #assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+    
+    # parameters for width
+    self.Ranges = []
+    self.layer2indexRange = []
+    for i, layer in enumerate(self.layers):
+      start_index = len(self.Ranges)
+      self.Ranges += layer.get_range()
+      self.layer2indexRange.append( (start_index, len(self.Ranges)) )
+    assert len(self.Ranges) + 1 == depth, 'invalid depth check {:} vs {:}'.format(len(self.Ranges) + 1, depth)
+
+    self.register_parameter('width_attentions', nn.Parameter(torch.Tensor(len(self.Ranges), get_width_choices(None))))
+    self.register_parameter('depth_attentions', nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))))
+    nn.init.normal_(self.width_attentions, 0, 0.01)
+    nn.init.normal_(self.depth_attentions, 0, 0.01)
+    self.apply(initialize_resnet)
+
+  def arch_parameters(self, LR=None):
+    if LR is None:
+      return [self.width_attentions, self.depth_attentions]
+    else:
+      return [
+               {"params": self.width_attentions, "lr": LR},
+               {"params": self.depth_attentions, "lr": LR},
+             ]
+
+  def base_parameters(self):
+    return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
+
+  def get_flop(self, mode, config_dict, extra_info):
+    if config_dict is not None: config_dict = config_dict.copy()
+    # select channels 
+    channels = [3]
+    for i, weight in enumerate(self.width_attentions):
+      if mode == 'genotype':
+        with torch.no_grad():
+          probe = nn.functional.softmax(weight, dim=0)
+          C = self.Ranges[i][ torch.argmax(probe).item() ]
+      elif mode == 'max':
+        C = self.Ranges[i][-1]
+      elif mode == 'fix':
+        C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
+      elif mode == 'random':
+        assert isinstance(extra_info, float), 'invalid extra_info : {:}'.format(extra_info)
+        with torch.no_grad():
+          prob = nn.functional.softmax(weight, dim=0)
+          approximate_C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
+          for j in range(prob.size(0)):
+            prob[j] = 1 / (abs(j - (approximate_C-self.Ranges[i][j])) + 0.2)
+          C = self.Ranges[i][ torch.multinomial(prob, 1, False).item() ]
+      else:
+        raise ValueError('invalid mode : {:}'.format(mode))
+      channels.append( C )
+    # select depth
+    if mode == 'genotype':
+      with torch.no_grad():
+        depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
+    elif mode == 'max' or mode == 'fix':
+      choices = [depth_probs.size(1)-1 for _ in range(depth_probs.size(0))]
+    elif mode == 'random':
+      with torch.no_grad():
+        depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        choices = torch.multinomial(depth_probs, 1, False).cpu().tolist()
+    else:
+      raise ValueError('invalid mode : {:}'.format(mode))
+    selected_layers = []
+    for choice, xvalue in zip(choices, self.depth_info_list):
+      xtemp = xvalue[1]['choices'][choice] - xvalue[1]['xstart'] + 1
+      selected_layers.append(xtemp)
+    flop = 0
+    for i, layer in enumerate(self.layers):
+      s, e = self.layer2indexRange[i]
+      xchl = tuple( channels[s:e+1] )
+      if i in self.depth_at_i:
+        xstagei, xatti = self.depth_at_i[i]
+        if xatti <= choices[xstagei]: # leave this depth
+          flop+= layer.get_flops(xchl)
+        else:
+          flop+= 0 # do not use this layer
+      else:
+        flop+= layer.get_flops(xchl)
+    # the last fc layer
+    flop += channels[-1] * self.classifier.out_features
+    if config_dict is None:
+      return flop / 1e6
+    else:
+      config_dict['xchannels']  = channels
+      config_dict['xblocks']    = selected_layers
+      config_dict['super_type'] = 'infer-shape'
+      config_dict['estimated_FLOP'] = flop / 1e6
+      return flop / 1e6, config_dict
+
+  def get_arch_info(self):
+    string = "for depth and width, there are {:} + {:} attention probabilities.".format(len(self.depth_attentions), len(self.width_attentions))
+    string+= '\n{:}'.format(self.depth_info)
+    discrepancy = []
+    with torch.no_grad():
+      for i, att in enumerate(self.depth_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.depth_attentions), ' '.join(prob))
+        logt = ['{:.4f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:17s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || discrepancy={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+      string += '\n-----------------------------------------------'
+      for i, att in enumerate(self.width_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.width_attentions), ' '.join(prob))
+        logt = ['{:.3f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:52s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || dis={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+    return string, discrepancy
+
+  def set_tau(self, tau_max, tau_min, epoch_ratio):
+    assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
+    tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+    self.tau = tau
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, inputs):
+    flop_width_probs = nn.functional.softmax(self.width_attentions, dim=1)
+    flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+    flop_depth_probs = torch.flip( torch.cumsum( torch.flip(flop_depth_probs, [1]), 1 ), [1] )
+    selected_widths, selected_width_probs = select2withP(self.width_attentions, self.tau)
+    selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
+    with torch.no_grad():
+      selected_widths = selected_widths.cpu()
+
+    x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+    feature_maps = []
+    for i, layer in enumerate(self.layers):
+      selected_w_index = selected_widths     [last_channel_idx: last_channel_idx+layer.num_conv]
+      selected_w_probs = selected_width_probs[last_channel_idx: last_channel_idx+layer.num_conv]
+      layer_prob       = flop_width_probs    [last_channel_idx: last_channel_idx+layer.num_conv]
+      x, expected_inC, expected_flop = layer( (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) )
+      feature_maps.append( x )
+      last_channel_idx += layer.num_conv
+      if i in self.depth_info: # aggregate the information
+        choices = self.depth_info[i]['choices']
+        xstagei = self.depth_info[i]['stage']
+        #print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist()))
+        #for A, W in zip(choices, selected_depth_probs[xstagei]):
+        #  print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W))
+        possible_tensors = []
+        max_C = max( feature_maps[A].size(1) for A in choices )
+        for tempi, A in enumerate(choices):
+          xtensor = ChannelWiseInter(feature_maps[A], max_C)
+          #drop_ratio = 1-(tempi+1.0)/len(choices)
+          #xtensor = drop_path(xtensor, drop_ratio)
+          possible_tensors.append( xtensor )
+        weighted_sum = sum( xtensor * W for xtensor, W in zip(possible_tensors, selected_depth_probs[xstagei]) )
+        x = weighted_sum
+        
+      if i in self.depth_at_i:
+        xstagei, xatti = self.depth_at_i[i]
+        x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop
+      else:
+        x_expected_flop = expected_flop
+      flops.append( x_expected_flop )
+    flops.append( expected_inC * (self.classifier.out_features*1.0/1e6) )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = linear_forward(features, self.classifier)
+    return logits, torch.stack( [sum(flops)] )
+
+  def basic_forward(self, inputs):
+    if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_searchs/SearchCifarResNet_depth.py

@@ -0,0 +1,340 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+from collections import OrderedDict
+from bisect import bisect_right
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils    import additive_func
+from .SoftSelect      import select2withP, ChannelWiseInter
+from .SoftSelect      import linear_forward
+from .SoftSelect      import get_width_choices
+
+
+def get_depth_choices(nDepth, return_num):
+  if nDepth == 2:
+    choices = (1, 2)
+  elif nDepth == 3:
+    choices = (1, 2, 3)
+  elif nDepth > 3:
+    choices = list(range(1, nDepth+1, 2))
+    if choices[-1] < nDepth: choices.append(nDepth)
+  else:
+    raise ValueError('invalid nDepth : {:}'.format(nDepth))
+  if return_num: return len(choices)
+  else         : return choices
+
+
+
+class ConvBNReLU(nn.Module):
+  num_conv  = 1
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    self.InShape  = None
+    self.OutShape = None
+    self.choices  = get_width_choices(nOut)
+    self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
+
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    else       : self.bn  = None
+    if has_relu: self.relu = nn.ReLU(inplace=False)
+    else       : self.relu = None
+    self.in_dim   = nIn
+    self.out_dim  = nOut
+
+  def get_flops(self, divide=1):
+    iC, oC = self.in_dim, self.out_dim
+    assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:}  |  {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
+    assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
+    assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
+    #conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+    conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
+    all_positions = self.OutShape[0] * self.OutShape[1]
+    flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+    if self.conv.bias is not None: flops += all_positions / divide
+    return flops
+
+  def forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.bn  : out = self.bn( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    if self.InShape is None:
+      self.InShape  = (inputs.size(-2), inputs.size(-1))
+      self.OutShape = (out.size(-2)   , out.size(-1))
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  expansion = 1
+  num_conv  = 2
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(  planes, planes, 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes
+    self.search_mode = 'basic'
+
+  def get_flops(self, divide=1):
+    flop_A = self.conv_a.get_flops(divide)
+    flop_B = self.conv_b.get_flops(divide)
+    if hasattr(self.downsample, 'get_flops'):
+      flop_C = self.downsample.get_flops(divide)
+    else:
+      flop_C = 0
+    return flop_A + flop_B + flop_C
+
+  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
+    out = additive_func(residual, basicblock)
+    return nn.functional.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_1x1 = ConvBNReLU(inplanes, planes, 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(  planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes*self.expansion:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes * self.expansion
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_1x1.get_range() + self.conv_3x3.get_range() + self.conv_1x4.get_range()
+
+  def get_flops(self, divide):
+    flop_A = self.conv_1x1.get_flops(divide)
+    flop_B = self.conv_3x3.get_flops(divide)
+    flop_C = self.conv_1x4.get_flops(divide)
+    if hasattr(self.downsample, 'get_flops'):
+      flop_D = self.downsample.get_flops(divide)
+    else:
+      flop_D = 0
+    return flop_A + flop_B + flop_C + flop_D
+
+  def forward(self, inputs):
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+    if self.downsample is not None: residual = self.downsample(inputs)
+    else                          : residual = inputs
+    out = additive_func(residual, bottleneck)
+    return nn.functional.relu(out, inplace=True)
+
+
+class SearchDepthCifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, num_classes):
+    super(SearchDepthCifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+
+    self.message      = 'SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes  = num_classes
+    self.channels     = [16]
+    self.layers       = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    self.InShape      = None
+    self.depth_info   = OrderedDict()
+    self.depth_at_i   = OrderedDict()
+    for stage in range(3):
+      cur_block_choices = get_depth_choices(layer_blocks, False)
+      assert cur_block_choices[-1] == layer_blocks, 'stage={:}, {:} vs {:}'.format(stage, cur_block_choices, layer_blocks)
+      self.message += "\nstage={:} ::: depth-block-choices={:} for {:} blocks.".format(stage, cur_block_choices, layer_blocks)
+      block_choices, xstart = [], len(self.layers)
+      for iL in range(layer_blocks):
+        iC     = self.channels[-1]
+        planes = 16 * (2**stage)
+        stride = 2 if stage > 0 and iL == 0 else 1
+        module = block(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
+        # added for depth
+        layer_index = len(self.layers) - 1
+        if iL + 1 in cur_block_choices: block_choices.append( layer_index )
+        if iL + 1 == layer_blocks:
+          self.depth_info[layer_index] = {'choices': block_choices,
+                                          'stage'  : stage,
+                                          'xstart' : xstart}
+    self.depth_info_list = []
+    for xend, info in self.depth_info.items():
+      self.depth_info_list.append( (xend, info) )
+      xstart, xstage = info['xstart'], info['stage']
+      for ilayer in range(xstart, xend+1):
+        idx = bisect_right(info['choices'], ilayer-1)
+        self.depth_at_i[ilayer] = (xstage, idx)
+
+    self.avgpool     = nn.AvgPool2d(8)
+    self.classifier  = nn.Linear(module.out_dim, num_classes)
+    self.InShape     = None
+    self.tau         = -1
+    self.search_mode = 'basic'
+    #assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+    
+
+    self.register_parameter('depth_attentions', nn.Parameter(torch.Tensor(3, get_depth_choices(layer_blocks, True))))
+    nn.init.normal_(self.depth_attentions, 0, 0.01)
+    self.apply(initialize_resnet)
+
+  def arch_parameters(self):
+    return [self.depth_attentions]
+
+  def base_parameters(self):
+    return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
+
+  def get_flop(self, mode, config_dict, extra_info):
+    if config_dict is not None: config_dict = config_dict.copy()
+    # select depth
+    if mode == 'genotype':
+      with torch.no_grad():
+        depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
+    elif mode == 'max':
+      choices = [depth_probs.size(1)-1 for _ in range(depth_probs.size(0))]
+    elif mode == 'random':
+      with torch.no_grad():
+        depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        choices = torch.multinomial(depth_probs, 1, False).cpu().tolist()
+    else:
+      raise ValueError('invalid mode : {:}'.format(mode))
+    selected_layers = []
+    for choice, xvalue in zip(choices, self.depth_info_list):
+      xtemp = xvalue[1]['choices'][choice] - xvalue[1]['xstart'] + 1
+      selected_layers.append(xtemp)
+    flop = 0
+    for i, layer in enumerate(self.layers):
+      if i in self.depth_at_i:
+        xstagei, xatti = self.depth_at_i[i]
+        if xatti <= choices[xstagei]: # leave this depth
+          flop+= layer.get_flops()
+        else:
+          flop+= 0 # do not use this layer
+      else:
+        flop+= layer.get_flops()
+    # the last fc layer
+    flop += self.classifier.in_features * self.classifier.out_features
+    if config_dict is None:
+      return flop / 1e6
+    else:
+      config_dict['xblocks']    = selected_layers
+      config_dict['super_type'] = 'infer-depth'
+      config_dict['estimated_FLOP'] = flop / 1e6
+      return flop / 1e6, config_dict
+
+  def get_arch_info(self):
+    string = "for depth, there are {:} attention probabilities.".format(len(self.depth_attentions))
+    string+= '\n{:}'.format(self.depth_info)
+    discrepancy = []
+    with torch.no_grad():
+      for i, att in enumerate(self.depth_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.depth_attentions), ' '.join(prob))
+        logt = ['{:.4f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:17s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || discrepancy={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+    return string, discrepancy
+
+  def set_tau(self, tau_max, tau_min, epoch_ratio):
+    assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
+    tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+    self.tau = tau
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, inputs):
+    flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+    flop_depth_probs = torch.flip( torch.cumsum( torch.flip(flop_depth_probs, [1]), 1 ), [1] )
+    selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
+
+    x, flops = inputs, []
+    feature_maps = []
+    for i, layer in enumerate(self.layers):
+      layer_i = layer( x )
+      feature_maps.append( layer_i )
+      if i in self.depth_info: # aggregate the information
+        choices = self.depth_info[i]['choices']
+        xstagei = self.depth_info[i]['stage']
+        possible_tensors = []
+        for tempi, A in enumerate(choices):
+          xtensor = feature_maps[A]
+          possible_tensors.append( xtensor )
+        weighted_sum = sum( xtensor * W for xtensor, W in zip(possible_tensors, selected_depth_probs[xstagei]) )
+        x = weighted_sum
+      else:
+        x = layer_i
+       
+      if i in self.depth_at_i:
+        xstagei, xatti = self.depth_at_i[i]
+        #print ('layer-{:03d}, stage={:}, att={:}, prob={:}, flop={:}'.format(i, xstagei, xatti, flop_depth_probs[xstagei, xatti].item(), layer.get_flops(1e6)))
+        x_expected_flop = flop_depth_probs[xstagei, xatti] * layer.get_flops(1e6)
+      else:
+        x_expected_flop = layer.get_flops(1e6)
+      flops.append( x_expected_flop )
+    flops.append( (self.classifier.in_features * self.classifier.out_features*1.0/1e6) )
+
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = linear_forward(features, self.classifier)
+    return logits, torch.stack( [sum(flops)] )
+
+  def basic_forward(self, inputs):
+    if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_searchs/SearchCifarResNet_width.py

@@ -0,0 +1,393 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils    import additive_func
+from .SoftSelect      import select2withP, ChannelWiseInter
+from .SoftSelect      import linear_forward
+from .SoftSelect      import get_width_choices as get_choices
+
+
+def conv_forward(inputs, conv, choices):
+  iC = conv.in_channels
+  fill_size = list(inputs.size())
+  fill_size[1] = iC - fill_size[1]
+  filled  = torch.zeros(fill_size, device=inputs.device)
+  xinputs = torch.cat((inputs, filled), dim=1)
+  outputs = conv(xinputs)
+  selecteds = [outputs[:,:oC] for oC in choices]
+  return selecteds
+
+
+class ConvBNReLU(nn.Module):
+  num_conv  = 1
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    self.InShape  = None
+    self.OutShape = None
+    self.choices  = get_choices(nOut)
+    self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
+
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    #if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    #else       : self.bn  = None
+    self.has_bn = has_bn
+    self.BNs  = nn.ModuleList()
+    for i, _out in enumerate(self.choices):
+      self.BNs.append(nn.BatchNorm2d(_out))
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+    self.in_dim   = nIn
+    self.out_dim  = nOut
+    self.search_mode = 'basic'
+
+  def get_flops(self, channels, check_range=True, divide=1):
+    iC, oC = channels
+    if check_range: assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:}  |  {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
+    assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
+    assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
+    #conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+    conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
+    all_positions = self.OutShape[0] * self.OutShape[1]
+    flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+    if self.conv.bias is not None: flops += all_positions / divide
+    return flops
+
+  def get_range(self):
+    return [self.choices]
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, index, prob = tuple_inputs
+    index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+    probability = torch.squeeze(probability)
+    assert len(index) == 2, 'invalid length : {:}'.format(index)
+    # compute expected flop
+    #coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+    expected_outC = (self.choices_tensor * probability).sum()
+    expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    # convolutional layer
+    out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+    out_bns   = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+    # merge
+    out_channel = max([x.size(1) for x in out_bns])
+    outA = ChannelWiseInter(out_bns[0], out_channel)
+    outB = ChannelWiseInter(out_bns[1], out_channel)
+    out  = outA * prob[0] + outB * prob[1]
+    #out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    return out, expected_outC, expected_flop
+
+  def basic_forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.has_bn:out= self.BNs[-1]( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    if self.InShape is None:
+      self.InShape  = (inputs.size(-2), inputs.size(-1))
+      self.OutShape = (out.size(-2)   , out.size(-1))
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  expansion = 1
+  num_conv  = 2
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(  planes, planes, 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_a.get_range() + self.conv_b.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 3, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv_a.get_flops([channels[0], channels[1]])
+    flop_B = self.conv_b.get_flops([channels[1], channels[2]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_C = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_C = channels[0] * channels[-1] * self.conv_b.OutShape[0] * self.conv_b.OutShape[1]
+    return flop_A + flop_B + flop_C
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
+    out_a, expected_inC_a, expected_flop_a = self.conv_a( (inputs, expected_inC  , probability[0], indexes[0], probs[0]) )
+    out_b, expected_inC_b, expected_flop_b = self.conv_b( (out_a , expected_inC_a, probability[1], indexes[1], probs[1]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[1], indexes[1], probs[1]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out_b)
+    return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
+
+  def basic_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
+    out = additive_func(residual, basicblock)
+    return nn.functional.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_1x1 = ConvBNReLU(inplanes, planes, 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(  planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes*self.expansion:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes * self.expansion
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_1x1.get_range() + self.conv_3x3.get_range() + self.conv_1x4.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 4, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
+    flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
+    flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_D = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_D = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_D = channels[0] * channels[-1] * self.conv_1x4.OutShape[0] * self.conv_1x4.OutShape[1]
+    return flop_A + flop_B + flop_C + flop_D
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def basic_forward(self, inputs):
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+    if self.downsample is not None: residual = self.downsample(inputs)
+    else                          : residual = inputs
+    out = additive_func(residual, bottleneck)
+    return nn.functional.relu(out, inplace=True)
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
+    out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( (inputs, expected_inC    , probability[0], indexes[0], probs[0]) )
+    out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( (out_1x1,expected_inC_1x1, probability[1], indexes[1], probs[1]) )
+    out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( (out_3x3,expected_inC_3x3, probability[2], indexes[2], probs[2]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[2], indexes[2], probs[2]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out_1x4)
+    return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
+
+
+class SearchWidthCifarResNet(nn.Module):
+
+  def __init__(self, block_name, depth, num_classes):
+    super(SearchWidthCifarResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'ResNetBasicblock':
+      block = ResNetBasicblock
+      assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110'
+      layer_blocks = (depth - 2) // 6
+    elif block_name == 'ResNetBottleneck':
+      block = ResNetBottleneck
+      assert (depth - 2) % 9 == 0, 'depth should be one of 164'
+      layer_blocks = (depth - 2) // 9
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+
+    self.message     = 'SearchWidthCifarResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes = num_classes
+    self.channels    = [16]
+    self.layers      = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    self.InShape     = None
+    for stage in range(3):
+      for iL in range(layer_blocks):
+        iC     = self.channels[-1]
+        planes = 16 * (2**stage)
+        stride = 2 if stage > 0 and iL == 0 else 1
+        module = block(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
+  
+    self.avgpool     = nn.AvgPool2d(8)
+    self.classifier  = nn.Linear(module.out_dim, num_classes)
+    self.InShape     = None
+    self.tau         = -1
+    self.search_mode = 'basic'
+    #assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+    
+    # parameters for width
+    self.Ranges = []
+    self.layer2indexRange = []
+    for i, layer in enumerate(self.layers):
+      start_index = len(self.Ranges)
+      self.Ranges += layer.get_range()
+      self.layer2indexRange.append( (start_index, len(self.Ranges)) )
+    assert len(self.Ranges) + 1 == depth, 'invalid depth check {:} vs {:}'.format(len(self.Ranges) + 1, depth)
+
+    self.register_parameter('width_attentions', nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))))
+    nn.init.normal_(self.width_attentions, 0, 0.01)
+    self.apply(initialize_resnet)
+
+  def arch_parameters(self):
+    return [self.width_attentions]
+
+  def base_parameters(self):
+    return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
+
+  def get_flop(self, mode, config_dict, extra_info):
+    if config_dict is not None: config_dict = config_dict.copy()
+    #weights = [F.softmax(x, dim=0) for x in self.width_attentions]
+    channels = [3]
+    for i, weight in enumerate(self.width_attentions):
+      if mode == 'genotype':
+        with torch.no_grad():
+          probe = nn.functional.softmax(weight, dim=0)
+          C = self.Ranges[i][ torch.argmax(probe).item() ]
+      elif mode == 'max':
+        C = self.Ranges[i][-1]
+      elif mode == 'fix':
+        C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
+      elif mode == 'random':
+        assert isinstance(extra_info, float), 'invalid extra_info : {:}'.format(extra_info)
+        with torch.no_grad():
+          prob = nn.functional.softmax(weight, dim=0)
+          approximate_C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
+          for j in range(prob.size(0)):
+            prob[j] = 1 / (abs(j - (approximate_C-self.Ranges[i][j])) + 0.2)
+          C = self.Ranges[i][ torch.multinomial(prob, 1, False).item() ]
+      else:
+        raise ValueError('invalid mode : {:}'.format(mode))
+      channels.append( C )
+    flop = 0
+    for i, layer in enumerate(self.layers):
+      s, e = self.layer2indexRange[i]
+      xchl = tuple( channels[s:e+1] )
+      flop+= layer.get_flops(xchl)
+    # the last fc layer
+    flop += channels[-1] * self.classifier.out_features
+    if config_dict is None:
+      return flop / 1e6
+    else:
+      config_dict['xchannels']  = channels
+      config_dict['super_type'] = 'infer-width'
+      config_dict['estimated_FLOP'] = flop / 1e6
+      return flop / 1e6, config_dict
+
+  def get_arch_info(self):
+    string = "for width, there are {:} attention probabilities.".format(len(self.width_attentions))
+    discrepancy = []
+    with torch.no_grad():
+      for i, att in enumerate(self.width_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.width_attentions), ' '.join(prob))
+        logt = ['{:.3f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:52s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || dis={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+    return string, discrepancy
+
+  def set_tau(self, tau_max, tau_min, epoch_ratio):
+    assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
+    tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+    self.tau = tau
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, inputs):
+    flop_probs = nn.functional.softmax(self.width_attentions, dim=1)
+    selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
+    with torch.no_grad():
+      selected_widths = selected_widths.cpu()
+
+    x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+    for i, layer in enumerate(self.layers):
+      selected_w_index = selected_widths[last_channel_idx: last_channel_idx+layer.num_conv]
+      selected_w_probs = selected_probs[last_channel_idx: last_channel_idx+layer.num_conv]
+      layer_prob       = flop_probs[last_channel_idx: last_channel_idx+layer.num_conv]
+      x, expected_inC, expected_flop = layer( (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) )
+      last_channel_idx += layer.num_conv
+      flops.append( expected_flop )
+    flops.append( expected_inC * (self.classifier.out_features*1.0/1e6) )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = linear_forward(features, self.classifier)
+    return logits, torch.stack( [sum(flops)] )
+
+  def basic_forward(self, inputs):
+    if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_searchs/SearchImagenetResNet.py

@@ -0,0 +1,482 @@
+import math, torch
+from collections import OrderedDict
+from bisect import bisect_right
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils    import additive_func
+from .SoftSelect      import select2withP, ChannelWiseInter
+from .SoftSelect      import linear_forward
+from .SoftSelect      import get_width_choices
+
+
+def get_depth_choices(layers):
+  min_depth = min(layers)
+  info = {'num': min_depth}
+  for i, depth in enumerate(layers):
+    choices = []
+    for j in range(1, min_depth+1):
+      choices.append( int( float(depth)*j/min_depth ) )
+    info[i] = choices
+  return info
+
+
+def conv_forward(inputs, conv, choices):
+  iC = conv.in_channels
+  fill_size = list(inputs.size())
+  fill_size[1] = iC - fill_size[1]
+  filled  = torch.zeros(fill_size, device=inputs.device)
+  xinputs = torch.cat((inputs, filled), dim=1)
+  outputs = conv(xinputs)
+  selecteds = [outputs[:,:oC] for oC in choices]
+  return selecteds
+
+
+class ConvBNReLU(nn.Module):
+  num_conv  = 1
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu, last_max_pool=False):
+    super(ConvBNReLU, self).__init__()
+    self.InShape  = None
+    self.OutShape = None
+    self.choices  = get_width_choices(nOut)
+    self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
+
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    #if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    #else       : self.bn  = None
+    self.has_bn = has_bn
+    self.BNs  = nn.ModuleList()
+    for i, _out in enumerate(self.choices):
+      self.BNs.append(nn.BatchNorm2d(_out))
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+  
+    if last_max_pool: self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+    else            : self.maxpool = None
+    self.in_dim   = nIn
+    self.out_dim  = nOut
+    self.search_mode = 'basic'
+
+  def get_flops(self, channels, check_range=True, divide=1):
+    iC, oC = channels
+    if check_range: assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:}  |  {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
+    assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
+    assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
+    #conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+    conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
+    all_positions = self.OutShape[0] * self.OutShape[1]
+    flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+    if self.conv.bias is not None: flops += all_positions / divide
+    return flops
+
+  def get_range(self):
+    return [self.choices]
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, index, prob = tuple_inputs
+    index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+    probability = torch.squeeze(probability)
+    assert len(index) == 2, 'invalid length : {:}'.format(index)
+    # compute expected flop
+    #coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+    expected_outC = (self.choices_tensor * probability).sum()
+    expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    # convolutional layer
+    out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+    out_bns   = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+    # merge
+    out_channel = max([x.size(1) for x in out_bns])
+    outA = ChannelWiseInter(out_bns[0], out_channel)
+    outB = ChannelWiseInter(out_bns[1], out_channel)
+    out  = outA * prob[0] + outB * prob[1]
+    #out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+    if self.relu   : out = self.relu( out )
+    if self.maxpool: out = self.maxpool(out)
+    return out, expected_outC, expected_flop
+
+  def basic_forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.has_bn:out= self.BNs[-1]( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    if self.InShape is None:
+      self.InShape  = (inputs.size(-2), inputs.size(-1))
+      self.OutShape = (out.size(-2)   , out.size(-1))
+    if self.maxpool: out = self.maxpool(out)
+    return out
+
+
+class ResNetBasicblock(nn.Module):
+  expansion = 1
+  num_conv  = 2
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBasicblock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_a = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_b = ConvBNReLU(  planes, planes, 3,      1, 1, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=True, has_relu=False)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True, has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_a.get_range() + self.conv_b.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 3, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv_a.get_flops([channels[0], channels[1]])
+    flop_B = self.conv_b.get_flops([channels[1], channels[2]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_C = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_C = channels[0] * channels[-1] * self.conv_b.OutShape[0] * self.conv_b.OutShape[1]
+    return flop_A + flop_B + flop_C
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 2 and probs.size(0) == 2 and probability.size(0) == 2
+    #import pdb; pdb.set_trace()
+    out_a, expected_inC_a, expected_flop_a = self.conv_a( (inputs, expected_inC  , probability[0], indexes[0], probs[0]) )
+    out_b, expected_inC_b, expected_flop_b = self.conv_b( (out_a , expected_inC_a, probability[1], indexes[1], probs[1]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[1], indexes[1], probs[1]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out_b)
+    return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
+
+  def basic_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
+    out = additive_func(residual, basicblock)
+    return nn.functional.relu(out, inplace=True)
+
+
+
+class ResNetBottleneck(nn.Module):
+  expansion = 4
+  num_conv  = 3
+  def __init__(self, inplanes, planes, stride):
+    super(ResNetBottleneck, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv_1x1 = ConvBNReLU(inplanes, planes, 1,      1, 0, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_3x3 = ConvBNReLU(  planes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    self.conv_1x4 = ConvBNReLU(planes, planes*self.expansion, 1, 1, 0, False, has_avg=False, has_bn=True, has_relu=False)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=True, has_bn=True, has_relu=False)
+    elif inplanes != planes*self.expansion:
+      self.downsample = ConvBNReLU(inplanes, planes*self.expansion, 1, 1, 0, False, has_avg=False,has_bn=True, has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes * self.expansion
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv_1x1.get_range() + self.conv_3x3.get_range() + self.conv_1x4.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 4, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv_1x1.get_flops([channels[0], channels[1]])
+    flop_B = self.conv_3x3.get_flops([channels[1], channels[2]])
+    flop_C = self.conv_1x4.get_flops([channels[2], channels[3]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_D = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_D = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_D = channels[0] * channels[-1] * self.conv_1x4.OutShape[0] * self.conv_1x4.OutShape[1]
+    return flop_A + flop_B + flop_C + flop_D
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def basic_forward(self, inputs):
+    bottleneck = self.conv_1x1(inputs)
+    bottleneck = self.conv_3x3(bottleneck)
+    bottleneck = self.conv_1x4(bottleneck)
+    if self.downsample is not None: residual = self.downsample(inputs)
+    else                          : residual = inputs
+    out = additive_func(residual, bottleneck)
+    return nn.functional.relu(out, inplace=True)
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 3 and probs.size(0) == 3 and probability.size(0) == 3
+    out_1x1, expected_inC_1x1, expected_flop_1x1 = self.conv_1x1( (inputs, expected_inC    , probability[0], indexes[0], probs[0]) )
+    out_3x3, expected_inC_3x3, expected_flop_3x3 = self.conv_3x3( (out_1x1,expected_inC_1x1, probability[1], indexes[1], probs[1]) )
+    out_1x4, expected_inC_1x4, expected_flop_1x4 = self.conv_1x4( (out_3x3,expected_inC_3x3, probability[2], indexes[2], probs[2]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[2], indexes[2], probs[2]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out_1x4)
+    return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
+
+
+class SearchShapeImagenetResNet(nn.Module):
+
+  def __init__(self, block_name, layers, deep_stem, num_classes):
+    super(SearchShapeImagenetResNet, self).__init__()
+
+    #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
+    if block_name == 'BasicBlock':
+      block = ResNetBasicblock
+    elif block_name == 'Bottleneck':
+      block = ResNetBottleneck
+    else:
+      raise ValueError('invalid block : {:}'.format(block_name))
+    
+    self.message      = 'SearchShapeCifarResNet : Depth : {:} , Layers for each block : {:}'.format(sum(layers)*block.num_conv, layers)
+    self.num_classes  = num_classes
+    if not deep_stem:
+      self.layers       = nn.ModuleList( [ ConvBNReLU(3, 64, 7, 2, 3, False, has_avg=False, has_bn=True, has_relu=True, last_max_pool=True) ] )
+      self.channels     = [64]
+    else:
+      self.layers       = nn.ModuleList( [ ConvBNReLU(3, 32, 3, 2, 1, False, has_avg=False, has_bn=True, has_relu=True)
+                                          ,ConvBNReLU(32,64, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True, last_max_pool=True) ] )
+      self.channels     = [32, 64]
+
+    meta_depth_info   = get_depth_choices(layers)
+    self.InShape      = None
+    self.depth_info   = OrderedDict()
+    self.depth_at_i   = OrderedDict()
+    for stage, layer_blocks in enumerate(layers):
+      cur_block_choices = meta_depth_info[stage]
+      assert cur_block_choices[-1] == layer_blocks, 'stage={:}, {:} vs {:}'.format(stage, cur_block_choices, layer_blocks)
+      block_choices, xstart = [], len(self.layers)
+      for iL in range(layer_blocks):
+        iC     = self.channels[-1]
+        planes = 64 * (2**stage)
+        stride = 2 if stage > 0 and iL == 0 else 1
+        module = block(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
+        # added for depth
+        layer_index = len(self.layers) - 1
+        if iL + 1 in cur_block_choices: block_choices.append( layer_index )
+        if iL + 1 == layer_blocks:
+          self.depth_info[layer_index] = {'choices': block_choices,
+                                          'stage'  : stage,
+                                          'xstart' : xstart}
+    self.depth_info_list = []
+    for xend, info in self.depth_info.items():
+      self.depth_info_list.append( (xend, info) )
+      xstart, xstage = info['xstart'], info['stage']
+      for ilayer in range(xstart, xend+1):
+        idx = bisect_right(info['choices'], ilayer-1)
+        self.depth_at_i[ilayer] = (xstage, idx)
+
+    self.avgpool     = nn.AdaptiveAvgPool2d((1,1))
+    self.classifier  = nn.Linear(module.out_dim, num_classes)
+    self.InShape     = None
+    self.tau         = -1
+    self.search_mode = 'basic'
+    #assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+    
+    # parameters for width
+    self.Ranges = []
+    self.layer2indexRange = []
+    for i, layer in enumerate(self.layers):
+      start_index = len(self.Ranges)
+      self.Ranges += layer.get_range()
+      self.layer2indexRange.append( (start_index, len(self.Ranges)) )
+
+    self.register_parameter('width_attentions', nn.Parameter(torch.Tensor(len(self.Ranges), get_width_choices(None))))
+    self.register_parameter('depth_attentions', nn.Parameter(torch.Tensor(len(layers), meta_depth_info['num'])))
+    nn.init.normal_(self.width_attentions, 0, 0.01)
+    nn.init.normal_(self.depth_attentions, 0, 0.01)
+    self.apply(initialize_resnet)
+
+  def arch_parameters(self, LR=None):
+    if LR is None:
+      return [self.width_attentions, self.depth_attentions]
+    else:
+      return [
+               {"params": self.width_attentions, "lr": LR},
+               {"params": self.depth_attentions, "lr": LR},
+             ]
+
+  def base_parameters(self):
+    return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
+
+  def get_flop(self, mode, config_dict, extra_info):
+    if config_dict is not None: config_dict = config_dict.copy()
+    # select channels 
+    channels = [3]
+    for i, weight in enumerate(self.width_attentions):
+      if mode == 'genotype':
+        with torch.no_grad():
+          probe = nn.functional.softmax(weight, dim=0)
+          C = self.Ranges[i][ torch.argmax(probe).item() ]
+      else:
+        raise ValueError('invalid mode : {:}'.format(mode))
+      channels.append( C )
+    # select depth
+    if mode == 'genotype':
+      with torch.no_grad():
+        depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+        choices = torch.argmax(depth_probs, dim=1).cpu().tolist()
+    else:
+      raise ValueError('invalid mode : {:}'.format(mode))
+    selected_layers = []
+    for choice, xvalue in zip(choices, self.depth_info_list):
+      xtemp = xvalue[1]['choices'][choice] - xvalue[1]['xstart'] + 1
+      selected_layers.append(xtemp)
+    flop = 0
+    for i, layer in enumerate(self.layers):
+      s, e = self.layer2indexRange[i]
+      xchl = tuple( channels[s:e+1] )
+      if i in self.depth_at_i:
+        xstagei, xatti = self.depth_at_i[i]
+        if xatti <= choices[xstagei]: # leave this depth
+          flop+= layer.get_flops(xchl)
+        else:
+          flop+= 0 # do not use this layer
+      else:
+        flop+= layer.get_flops(xchl)
+    # the last fc layer
+    flop += channels[-1] * self.classifier.out_features
+    if config_dict is None:
+      return flop / 1e6
+    else:
+      config_dict['xchannels']  = channels
+      config_dict['xblocks']    = selected_layers
+      config_dict['super_type'] = 'infer-shape'
+      config_dict['estimated_FLOP'] = flop / 1e6
+      return flop / 1e6, config_dict
+
+  def get_arch_info(self):
+    string = "for depth and width, there are {:} + {:} attention probabilities.".format(len(self.depth_attentions), len(self.width_attentions))
+    string+= '\n{:}'.format(self.depth_info)
+    discrepancy = []
+    with torch.no_grad():
+      for i, att in enumerate(self.depth_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.depth_attentions), ' '.join(prob))
+        logt = ['{:.4f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:17s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || discrepancy={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+      string += '\n-----------------------------------------------'
+      for i, att in enumerate(self.width_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.width_attentions), ' '.join(prob))
+        logt = ['{:.3f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:52s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || dis={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+    return string, discrepancy
+
+  def set_tau(self, tau_max, tau_min, epoch_ratio):
+    assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
+    tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+    self.tau = tau
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, inputs):
+    flop_width_probs = nn.functional.softmax(self.width_attentions, dim=1)
+    flop_depth_probs = nn.functional.softmax(self.depth_attentions, dim=1)
+    flop_depth_probs = torch.flip( torch.cumsum( torch.flip(flop_depth_probs, [1]), 1 ), [1] )
+    selected_widths, selected_width_probs = select2withP(self.width_attentions, self.tau)
+    selected_depth_probs = select2withP(self.depth_attentions, self.tau, True)
+    with torch.no_grad():
+      selected_widths = selected_widths.cpu()
+
+    x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+    feature_maps = []
+    for i, layer in enumerate(self.layers):
+      selected_w_index = selected_widths     [last_channel_idx: last_channel_idx+layer.num_conv]
+      selected_w_probs = selected_width_probs[last_channel_idx: last_channel_idx+layer.num_conv]
+      layer_prob       = flop_width_probs    [last_channel_idx: last_channel_idx+layer.num_conv]
+      x, expected_inC, expected_flop = layer( (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) )
+      feature_maps.append( x )
+      last_channel_idx += layer.num_conv
+      if i in self.depth_info: # aggregate the information
+        choices = self.depth_info[i]['choices']
+        xstagei = self.depth_info[i]['stage']
+        #print ('iL={:}, choices={:}, stage={:}, probs={:}'.format(i, choices, xstagei, selected_depth_probs[xstagei].cpu().tolist()))
+        #for A, W in zip(choices, selected_depth_probs[xstagei]):
+        #  print('Size = {:}, W = {:}'.format(feature_maps[A].size(), W))
+        possible_tensors = []
+        max_C = max( feature_maps[A].size(1) for A in choices )
+        for tempi, A in enumerate(choices):
+          xtensor = ChannelWiseInter(feature_maps[A], max_C)
+          possible_tensors.append( xtensor )
+        weighted_sum = sum( xtensor * W for xtensor, W in zip(possible_tensors, selected_depth_probs[xstagei]) )
+        x = weighted_sum
+        
+      if i in self.depth_at_i:
+        xstagei, xatti = self.depth_at_i[i]
+        x_expected_flop = flop_depth_probs[xstagei, xatti] * expected_flop
+      else:
+        x_expected_flop = expected_flop
+      flops.append( x_expected_flop )
+    flops.append( expected_inC * (self.classifier.out_features*1.0/1e6) )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = linear_forward(features, self.classifier)
+    return logits, torch.stack( [sum(flops)] )
+
+  def basic_forward(self, inputs):
+    if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_searchs/SearchSimResNet_width.py

@@ -0,0 +1,316 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+from ..initialization import initialize_resnet
+from ..SharedUtils    import additive_func
+from .SoftSelect      import select2withP, ChannelWiseInter
+from .SoftSelect      import linear_forward
+from .SoftSelect      import get_width_choices as get_choices
+
+
+def conv_forward(inputs, conv, choices):
+  iC = conv.in_channels
+  fill_size = list(inputs.size())
+  fill_size[1] = iC - fill_size[1]
+  filled  = torch.zeros(fill_size, device=inputs.device)
+  xinputs = torch.cat((inputs, filled), dim=1)
+  outputs = conv(xinputs)
+  selecteds = [outputs[:,:oC] for oC in choices]
+  return selecteds
+
+
+class ConvBNReLU(nn.Module):
+  num_conv  = 1
+  def __init__(self, nIn, nOut, kernel, stride, padding, bias, has_avg, has_bn, has_relu):
+    super(ConvBNReLU, self).__init__()
+    self.InShape  = None
+    self.OutShape = None
+    self.choices  = get_choices(nOut)
+    self.register_buffer('choices_tensor', torch.Tensor( self.choices ))
+
+    if has_avg : self.avg = nn.AvgPool2d(kernel_size=2, stride=2, padding=0)
+    else       : self.avg = None
+    self.conv = nn.Conv2d(nIn, nOut, kernel_size=kernel, stride=stride, padding=padding, dilation=1, groups=1, bias=bias)
+    #if has_bn  : self.bn  = nn.BatchNorm2d(nOut)
+    #else       : self.bn  = None
+    self.has_bn = has_bn
+    self.BNs  = nn.ModuleList()
+    for i, _out in enumerate(self.choices):
+      self.BNs.append(nn.BatchNorm2d(_out))
+    if has_relu: self.relu = nn.ReLU(inplace=True)
+    else       : self.relu = None
+    self.in_dim   = nIn
+    self.out_dim  = nOut
+    self.search_mode = 'basic'
+
+  def get_flops(self, channels, check_range=True, divide=1):
+    iC, oC = channels
+    if check_range: assert iC <= self.conv.in_channels and oC <= self.conv.out_channels, '{:} vs {:}  |  {:} vs {:}'.format(iC, self.conv.in_channels, oC, self.conv.out_channels)
+    assert isinstance(self.InShape, tuple) and len(self.InShape) == 2, 'invalid in-shape : {:}'.format(self.InShape)
+    assert isinstance(self.OutShape, tuple) and len(self.OutShape) == 2, 'invalid out-shape : {:}'.format(self.OutShape)
+    #conv_per_position_flops = self.conv.kernel_size[0] * self.conv.kernel_size[1] * iC * oC / self.conv.groups
+    conv_per_position_flops = (self.conv.kernel_size[0] * self.conv.kernel_size[1] * 1.0 / self.conv.groups)
+    all_positions = self.OutShape[0] * self.OutShape[1]
+    flops = (conv_per_position_flops * all_positions / divide) * iC * oC
+    if self.conv.bias is not None: flops += all_positions / divide
+    return flops
+
+  def get_range(self):
+    return [self.choices]
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, index, prob = tuple_inputs
+    index, prob = torch.squeeze(index).tolist(), torch.squeeze(prob)
+    probability = torch.squeeze(probability)
+    assert len(index) == 2, 'invalid length : {:}'.format(index)
+    # compute expected flop
+    #coordinates   = torch.arange(self.x_range[0], self.x_range[1]+1).type_as(probability)
+    expected_outC = (self.choices_tensor * probability).sum()
+    expected_flop = self.get_flops([expected_inC, expected_outC], False, 1e6)
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    # convolutional layer
+    out_convs = conv_forward(out, self.conv, [self.choices[i] for i in index])
+    out_bns   = [self.BNs[idx](out_conv) for idx, out_conv in zip(index, out_convs)]
+    # merge
+    out_channel = max([x.size(1) for x in out_bns])
+    outA = ChannelWiseInter(out_bns[0], out_channel)
+    outB = ChannelWiseInter(out_bns[1], out_channel)
+    out  = outA * prob[0] + outB * prob[1]
+    #out = additive_func(out_bns[0]*prob[0], out_bns[1]*prob[1])
+
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    return out, expected_outC, expected_flop
+
+  def basic_forward(self, inputs):
+    if self.avg : out = self.avg( inputs )
+    else        : out = inputs
+    conv = self.conv( out )
+    if self.has_bn:out= self.BNs[-1]( conv )
+    else        : out = conv
+    if self.relu: out = self.relu( out )
+    else        : out = out
+    if self.InShape is None:
+      self.InShape  = (inputs.size(-2), inputs.size(-1))
+      self.OutShape = (out.size(-2)   , out.size(-1))
+    return out
+
+
+class SimBlock(nn.Module):
+  expansion = 1
+  num_conv  = 1
+  def __init__(self, inplanes, planes, stride):
+    super(SimBlock, self).__init__()
+    assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+    self.conv = ConvBNReLU(inplanes, planes, 3, stride, 1, False, has_avg=False, has_bn=True, has_relu=True)
+    if stride == 2:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=True, has_bn=False, has_relu=False)
+    elif inplanes != planes:
+      self.downsample = ConvBNReLU(inplanes, planes, 1, 1, 0, False, has_avg=False,has_bn=True , has_relu=False)
+    else:
+      self.downsample = None
+    self.out_dim     = planes
+    self.search_mode = 'basic'
+
+  def get_range(self):
+    return self.conv.get_range()
+
+  def get_flops(self, channels):
+    assert len(channels) == 2, 'invalid channels : {:}'.format(channels)
+    flop_A = self.conv.get_flops([channels[0], channels[1]])
+    if hasattr(self.downsample, 'get_flops'):
+      flop_C = self.downsample.get_flops([channels[0], channels[-1]])
+    else:
+      flop_C = 0
+    if channels[0] != channels[-1] and self.downsample is None: # this short-cut will be added during the infer-train
+      flop_C = channels[0] * channels[-1] * self.conv.OutShape[0] * self.conv.OutShape[1]
+    return flop_A + flop_C
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic'   : return self.basic_forward(inputs)
+    elif self.search_mode == 'search': return self.search_forward(inputs)
+    else: raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, tuple_inputs):
+    assert isinstance(tuple_inputs, tuple) and len(tuple_inputs) == 5, 'invalid type input : {:}'.format( type(tuple_inputs) )
+    inputs, expected_inC, probability, indexes, probs = tuple_inputs
+    assert indexes.size(0) == 1 and probs.size(0) == 1 and probability.size(0) == 1, 'invalid size : {:}, {:}, {:}'.format(indexes.size(), probs.size(), probability.size())
+    out, expected_next_inC, expected_flop = self.conv( (inputs, expected_inC  , probability[0], indexes[0], probs[0]) )
+    if self.downsample is not None:
+      residual, _, expected_flop_c = self.downsample( (inputs, expected_inC  , probability[-1], indexes[-1], probs[-1]) )
+    else:
+      residual, expected_flop_c = inputs, 0
+    out = additive_func(residual, out)
+    return nn.functional.relu(out, inplace=True), expected_next_inC, sum([expected_flop, expected_flop_c])
+
+  def basic_forward(self, inputs):
+    basicblock = self.conv(inputs)
+    if self.downsample is not None: residual = self.downsample(inputs)
+    else                          : residual = inputs
+    out = additive_func(residual, basicblock)
+    return nn.functional.relu(out, inplace=True)
+
+
+
+class SearchWidthSimResNet(nn.Module):
+
+  def __init__(self, depth, num_classes):
+    super(SearchWidthSimResNet, self).__init__()
+
+    assert (depth - 2) % 3 == 0, 'depth should be one of 5, 8, 11, 14, ... instead of {:}'.format(depth)
+    layer_blocks = (depth - 2) // 3
+    self.message     = 'SearchWidthSimResNet : Depth : {:} , Layers for each block : {:}'.format(depth, layer_blocks)
+    self.num_classes = num_classes
+    self.channels    = [16]
+    self.layers      = nn.ModuleList( [ ConvBNReLU(3, 16, 3, 1, 1, False, has_avg=False, has_bn=True, has_relu=True) ] )
+    self.InShape     = None
+    for stage in range(3):
+      for iL in range(layer_blocks):
+        iC     = self.channels[-1]
+        planes = 16 * (2**stage)
+        stride = 2 if stage > 0 and iL == 0 else 1
+        module = SimBlock(iC, planes, stride)
+        self.channels.append( module.out_dim )
+        self.layers.append  ( module )
+        self.message += "\nstage={:}, ilayer={:02d}/{:02d}, block={:03d}, iC={:3d}, oC={:3d}, stride={:}".format(stage, iL, layer_blocks, len(self.layers)-1, iC, module.out_dim, stride)
+  
+    self.avgpool     = nn.AvgPool2d(8)
+    self.classifier  = nn.Linear(module.out_dim, num_classes)
+    self.InShape     = None
+    self.tau         = -1
+    self.search_mode = 'basic'
+    #assert sum(x.num_conv for x in self.layers) + 1 == depth, 'invalid depth check {:} vs {:}'.format(sum(x.num_conv for x in self.layers)+1, depth)
+    
+    # parameters for width
+    self.Ranges = []
+    self.layer2indexRange = []
+    for i, layer in enumerate(self.layers):
+      start_index = len(self.Ranges)
+      self.Ranges += layer.get_range()
+      self.layer2indexRange.append( (start_index, len(self.Ranges)) )
+    assert len(self.Ranges) + 1 == depth, 'invalid depth check {:} vs {:}'.format(len(self.Ranges) + 1, depth)
+
+    self.register_parameter('width_attentions', nn.Parameter(torch.Tensor(len(self.Ranges), get_choices(None))))
+    nn.init.normal_(self.width_attentions, 0, 0.01)
+    self.apply(initialize_resnet)
+
+  def arch_parameters(self):
+    return [self.width_attentions]
+
+  def base_parameters(self):
+    return list(self.layers.parameters()) + list(self.avgpool.parameters()) + list(self.classifier.parameters())
+
+  def get_flop(self, mode, config_dict, extra_info):
+    if config_dict is not None: config_dict = config_dict.copy()
+    #weights = [F.softmax(x, dim=0) for x in self.width_attentions]
+    channels = [3]
+    for i, weight in enumerate(self.width_attentions):
+      if mode == 'genotype':
+        with torch.no_grad():
+          probe = nn.functional.softmax(weight, dim=0)
+          C = self.Ranges[i][ torch.argmax(probe).item() ]
+      elif mode == 'max':
+        C = self.Ranges[i][-1]
+      elif mode == 'fix':
+        C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
+      elif mode == 'random':
+        assert isinstance(extra_info, float), 'invalid extra_info : {:}'.format(extra_info)
+        with torch.no_grad():
+          prob = nn.functional.softmax(weight, dim=0)
+          approximate_C = int( math.sqrt( extra_info ) * self.Ranges[i][-1] )
+          for j in range(prob.size(0)):
+            prob[j] = 1 / (abs(j - (approximate_C-self.Ranges[i][j])) + 0.2)
+          C = self.Ranges[i][ torch.multinomial(prob, 1, False).item() ]
+      else:
+        raise ValueError('invalid mode : {:}'.format(mode))
+      channels.append( C )
+    flop = 0
+    for i, layer in enumerate(self.layers):
+      s, e = self.layer2indexRange[i]
+      xchl = tuple( channels[s:e+1] )
+      flop+= layer.get_flops(xchl)
+    # the last fc layer
+    flop += channels[-1] * self.classifier.out_features
+    if config_dict is None:
+      return flop / 1e6
+    else:
+      config_dict['xchannels']  = channels
+      config_dict['super_type'] = 'infer-width'
+      config_dict['estimated_FLOP'] = flop / 1e6
+      return flop / 1e6, config_dict
+
+  def get_arch_info(self):
+    string = "for width, there are {:} attention probabilities.".format(len(self.width_attentions))
+    discrepancy = []
+    with torch.no_grad():
+      for i, att in enumerate(self.width_attentions):
+        prob = nn.functional.softmax(att, dim=0)
+        prob = prob.cpu() ; selc = prob.argmax().item() ; prob = prob.tolist()
+        prob = ['{:.3f}'.format(x) for x in prob]
+        xstring = '{:03d}/{:03d}-th : {:}'.format(i, len(self.width_attentions), ' '.join(prob))
+        logt = ['{:.3f}'.format(x) for x in att.cpu().tolist()]
+        xstring += '  ||  {:52s}'.format(' '.join(logt))
+        prob = sorted( [float(x) for x in prob] )
+        disc = prob[-1] - prob[-2]
+        xstring += '  || dis={:.2f} || select={:}/{:}'.format(disc, selc, len(prob))
+        discrepancy.append( disc )
+        string += '\n{:}'.format(xstring)
+    return string, discrepancy
+
+  def set_tau(self, tau_max, tau_min, epoch_ratio):
+    assert epoch_ratio >= 0 and epoch_ratio <= 1, 'invalid epoch-ratio : {:}'.format(epoch_ratio)
+    tau = tau_min + (tau_max-tau_min) * (1 + math.cos(math.pi * epoch_ratio)) / 2
+    self.tau = tau
+
+  def get_message(self):
+    return self.message
+
+  def forward(self, inputs):
+    if self.search_mode == 'basic':
+      return self.basic_forward(inputs)
+    elif self.search_mode == 'search':
+      return self.search_forward(inputs)
+    else:
+      raise ValueError('invalid search_mode = {:}'.format(self.search_mode))
+
+  def search_forward(self, inputs):
+    flop_probs = nn.functional.softmax(self.width_attentions, dim=1)
+    selected_widths, selected_probs = select2withP(self.width_attentions, self.tau)
+    with torch.no_grad():
+      selected_widths = selected_widths.cpu()
+
+    x, last_channel_idx, expected_inC, flops = inputs, 0, 3, []
+    for i, layer in enumerate(self.layers):
+      selected_w_index = selected_widths[last_channel_idx: last_channel_idx+layer.num_conv]
+      selected_w_probs = selected_probs[last_channel_idx: last_channel_idx+layer.num_conv]
+      layer_prob       = flop_probs[last_channel_idx: last_channel_idx+layer.num_conv]
+      x, expected_inC, expected_flop = layer( (x, expected_inC, layer_prob, selected_w_index, selected_w_probs) )
+      last_channel_idx += layer.num_conv
+      flops.append( expected_flop )
+    flops.append( expected_inC * (self.classifier.out_features*1.0/1e6) )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = linear_forward(features, self.classifier)
+    return logits, torch.stack( [sum(flops)] )
+
+  def basic_forward(self, inputs):
+    if self.InShape is None: self.InShape = (inputs.size(-2), inputs.size(-1))
+    x = inputs
+    for i, layer in enumerate(self.layers):
+      x = layer( x )
+    features = self.avgpool(x)
+    features = features.view(features.size(0), -1)
+    logits   = self.classifier(features)
+    return features, logits

models/shape_searchs/SoftSelect.py

@@ -0,0 +1,111 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import math, torch
+import torch.nn as nn
+
+
+def select2withP(logits, tau, just_prob=False, num=2, eps=1e-7):
+  if tau <= 0:
+    new_logits = logits
+    probs = nn.functional.softmax(new_logits, dim=1)
+  else       :
+    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)
+      if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break
+
+  if just_prob: return probs
+
+  #with torch.no_grad(): # add eps for unexpected torch error
+  #  probs = nn.functional.softmax(new_logits, dim=1)
+  #  selected_index = torch.multinomial(probs + eps, 2, False)
+  with torch.no_grad(): # add eps for unexpected torch error
+    probs          = probs.cpu()
+    selected_index = torch.multinomial(probs + eps, num, False).to(logits.device)
+  selected_logit = torch.gather(new_logits, 1, selected_index)
+  selcted_probs  = nn.functional.softmax(selected_logit, dim=1)
+  return selected_index, selcted_probs
+
+
+def ChannelWiseInter(inputs, oC, mode='v2'):
+  if mode == 'v1':
+    return ChannelWiseInterV1(inputs, oC)
+  elif mode == 'v2':
+    return ChannelWiseInterV2(inputs, oC)
+  else:
+    raise ValueError('invalid mode : {:}'.format(mode))
+
+
+def ChannelWiseInterV1(inputs, oC):
+  assert inputs.dim() == 4, 'invalid dimension : {:}'.format(inputs.size())
+  def start_index(a, b, c):
+    return int( math.floor(float(a * c) / b) )
+  def end_index(a, b, c):
+    return int( math.ceil(float((a + 1) * c) / b) )
+  batch, iC, H, W = inputs.size()
+  outputs = torch.zeros((batch, oC, H, W), dtype=inputs.dtype, device=inputs.device)
+  if iC == oC: return inputs
+  for ot in range(oC):
+    istartT, iendT = start_index(ot, oC, iC), end_index(ot, oC, iC)
+    values = inputs[:, istartT:iendT].mean(dim=1) 
+    outputs[:, ot, :, :] = values
+  return outputs
+
+
+def ChannelWiseInterV2(inputs, oC):
+  assert inputs.dim() == 4, 'invalid dimension : {:}'.format(inputs.size())
+  batch, C, H, W = inputs.size()
+  if C == oC: return inputs
+  else      : return nn.functional.adaptive_avg_pool3d(inputs, (oC,H,W))
+  #inputs_5D = inputs.view(batch, 1, C, H, W)
+  #otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'area', None)
+  #otputs    = otputs_5D.view(batch, oC, H, W)
+  #otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'trilinear', False)
+  #return otputs
+
+
+def linear_forward(inputs, linear):
+  if linear is None: return inputs
+  iC = inputs.size(1)
+  weight = linear.weight[:, :iC]
+  if linear.bias is None: bias = None
+  else                  : bias = linear.bias
+  return nn.functional.linear(inputs, weight, bias)
+
+
+def get_width_choices(nOut):
+  xsrange = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+  if nOut is None:
+    return len(xsrange)
+  else:
+    Xs = [int(nOut * i) for i in xsrange]
+    #xs = [ int(nOut * i // 10) for i in range(2, 11)]
+    #Xs = [x for i, x in enumerate(xs) if i+1 == len(xs) or xs[i+1] > x+1]
+    Xs = sorted( list( set(Xs) ) )
+    return tuple(Xs)
+
+
+def get_depth_choices(nDepth):
+  if nDepth is None:
+    return 3
+  else:
+    assert nDepth >= 3, 'nDepth should be greater than 2 vs {:}'.format(nDepth)
+    if nDepth == 1  : return (1, 1, 1)
+    elif nDepth == 2: return (1, 1, 2)
+    elif nDepth >= 3:
+      return (nDepth//3, nDepth*2//3, nDepth)
+    else:
+      raise ValueError('invalid Depth : {:}'.format(nDepth))
+
+
+def drop_path(x, drop_prob):
+  if drop_prob > 0.:
+    keep_prob = 1. - drop_prob
+    mask = x.new_zeros(x.size(0), 1, 1, 1)
+    mask = mask.bernoulli_(keep_prob)
+    x = x * (mask / keep_prob)
+    #x.div_(keep_prob)
+    #x.mul_(mask)
+  return x

models/shape_searchs/__init__.py

@@ -0,0 +1,8 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from .SearchCifarResNet_width import SearchWidthCifarResNet
+from .SearchCifarResNet_depth import SearchDepthCifarResNet
+from .SearchCifarResNet       import SearchShapeCifarResNet
+from .SearchSimResNet_width   import SearchWidthSimResNet
+from .SearchImagenetResNet    import SearchShapeImagenetResNet

models/shape_searchs/test.py

@@ -0,0 +1,20 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+import torch.nn as nn
+from SoftSelect import ChannelWiseInter
+
+
+if __name__ == '__main__':
+
+  tensors = torch.rand((16, 128, 7, 7))
+  
+  for oc in range(200, 210):
+    out_v1  = ChannelWiseInter(tensors, oc, 'v1')
+    out_v2  = ChannelWiseInter(tensors, oc, 'v2')
+    assert (out_v1 == out_v2).any().item() == 1
+  for oc in range(48, 160):
+    out_v1  = ChannelWiseInter(tensors, oc, 'v1')
+    out_v2  = ChannelWiseInter(tensors, oc, 'v2')
+    assert (out_v1 == out_v2).any().item() == 1