Update Auto-ReID

README.md

@@ -5,6 +5,7 @@ This project contains the following neural architecture search algorithms, imple
 - Network Pruning via Transformable Architecture Search, NeurIPS 2019
 - One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019
 - Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019
+- Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
 
 
 ## Requirements and Preparation
@@ -90,6 +91,12 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k GDAS_
 Searching codes come soon! A small example forward code segment for searching can be found in [this issue](https://github.com/D-X-Y/NAS-Projects/issues/12).
 
 
+## [Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification](https://arxiv.org/abs/1903.09776)
+
+The part-aware module is defined at [here](https://github.com/D-X-Y/NAS-Projects/blob/master/lib/models/cell_searchs/operations.py#L85).
+
+For more questions, please contact Ruijie Quan (Ruijie.Quan@student.uts.edu.au).
+
 # Citation
 
 If you find that this project helps your research, please consider citing some of the following papers:

lib/models/cell_searchs/operations.py

@@ -111,3 +111,52 @@ class FactorizedReduce(nn.Module):
 
   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