QAConv 2.1

* Simplified graph sampling
* Einstein summation for QAConv
* Hard triplet loss
* Adaptive epoch and learning rate scheduling
* Automatic mixed precision training

reid/evaluators.py

@@ -1,10 +1,10 @@
 from __future__ import print_function, absolute_import
+import sys
 import time
 from collections import OrderedDict
 
 import torch
 import numpy as np
-from .utils import to_torch
 
 from .evaluation_metrics import cmc, mean_ap
 from .tlift import TLift
@@ -23,10 +23,9 @@ def pre_tlift(gallery, query):
 
 def extract_cnn_feature(model, inputs):
     model = model.cuda().eval()
-    inputs = to_torch(inputs).cuda()
     with torch.no_grad():
         outputs = model(inputs)
-    outputs = outputs.data.cpu()
+    outputs = outputs.cpu()
     return outputs
 
 
@@ -180,14 +179,24 @@ def evaluate(self, matcher, testset, query_loader, gallery_loader, gal_batch_siz
                  prob_batch_size=4096, tau=100, sigma=200, K=10, alpha=0.2):
         query = testset.query
         gallery = testset.gallery
-        prob_fea, _ = extract_features(self.model, query_loader, verbose=True)
-        prob_fea = torch.cat([prob_fea[f].unsqueeze(0) for f, _, _, _ in query], 0)
-        gal_fea, _ = extract_features(self.model, gallery_loader, verbose=True)
-        gal_fea = torch.cat([gal_fea[f].unsqueeze(0) for f, _, _, _ in gallery], 0)
 
-        print('Compute similarity...', end='\t')
+        print('Compute similarity ...', end='\t')
         start = time.time()
-        dist = pairwise_distance(matcher, prob_fea, gal_fea, gal_batch_size, prob_batch_size)  # [p, g]
+
+        prob_fea, _ = extract_features(self.model, query_loader)
+        prob_fea = torch.cat([prob_fea[f].unsqueeze(0) for f, _, _, _ in query], 0)
+        num_prob = len(query)
+        num_gal = len(gallery)
+        batch_size = gallery_loader.batch_size
+        dist = torch.zeros(num_prob, num_gal)
+
+        for i, (imgs, fnames, pids, _) in enumerate(gallery_loader):
+            print('Compute similarity %d / %d. \t' % (i + 1, len(gallery_loader)), end='\r', file=sys.stdout.console)
+            gal_fea = extract_cnn_feature(self.model, imgs)
+            g0 = i * batch_size
+            g1 = min(num_gal, (i + 1) * batch_size)
+            dist[:, g0:g1] = pairwise_distance(matcher, prob_fea, gal_fea, batch_size, prob_batch_size)  # [p, g]
+
         print('Time: %.3f seconds.' % (time.time() - start))
         rank1, mAP = evaluate_all(dist, query=query, gallery=gallery)
 
@@ -204,6 +213,8 @@ def evaluate(self, matcher, testset, query_loader, gallery_loader, gal_batch_siz
                 dist_rerank[num_prob:, :num_prob] = dist.t()
                 dist_rerank[:num_prob, :num_prob] = pairwise_distance(matcher, prob_fea, prob_fea, gal_batch_size,
                                                                     prob_batch_size)
+                gal_fea, _ = extract_features(self.model, gallery_loader, verbose=True)
+                gal_fea = torch.cat([gal_fea[f].unsqueeze(0) for f, _, _, _ in gallery], 0)
                 dist_rerank[num_prob:, num_prob:] = pairwise_distance(matcher, gal_fea, gal_fea, gal_batch_size,
                                                                     prob_batch_size)
 

reid/loss/triplet_loss.py

@@ -0,0 +1,58 @@
+"""Class for the hard triplet loss
+    Shengcai Liao and Ling Shao, "Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification." In arXiv preprint, arXiv:2104.01546, 2021.
+    Author:
+        Shengcai Liao
+        scliao@ieee.org
+    Version:
+        V1.0
+        April 1, 2021
+    """
+
+import torch
+from torch.nn import Module
+from torch import nn
+
+
+class TripletLoss(Module):
+    def __init__(self, matcher, margin=16):
+        """
+        Inputs:
+            matcher: a class for matching pairs of images
+            margin: margin parameter for the triplet loss
+        """
+        super(TripletLoss, self).__init__()
+        self.matcher = matcher
+        self.margin = margin
+        self.ranking_loss = nn.MarginRankingLoss(margin=margin, reduction='none')
+
+    def reset_running_stats(self):
+        self.matcher.reset_running_stats()
+
+    def reset_parameters(self):
+        self.matcher.reset_parameters()
+
+    def _check_input_dim(self, input):
+        if input.dim() != 4:
+            raise ValueError('expected 4D input (got {}D input)'.format(input.dim()))
+
+    def forward(self, feature, target):
+        self._check_input_dim(feature)
+        self.matcher.make_kernel(feature)
+
+        score = self.matcher(feature)  # [b, b]
+
+        target1 = target.unsqueeze(1)
+        mask = (target1 == target1.t())
+        pair_labels = mask.float()
+
+        min_pos = torch.min(score * pair_labels + 
+                (1 - pair_labels + torch.eye(score.size(0), device=score.device)) * 1e15, dim=1)[0]
+        max_neg = torch.max(score * (1 - pair_labels) - pair_labels * 1e15, dim=1)[0]
+
+        # Compute ranking hinge loss
+        loss = self.ranking_loss(min_pos, max_neg, torch.ones_like(target))
+
+        with torch.no_grad():
+            acc = (min_pos >= max_neg).float()
+
+        return loss, acc

reid/models/qaconv.py

@@ -6,14 +6,13 @@
         Shengcai Liao
         scliao@ieee.org
     Version:
-        V1.2
-        Mar. 31, 2021
+        V1.3
+        July 1, 2021
     """
 
 import torch
 from torch import nn
 from torch.nn import Module
-from torch.nn import functional as F
 
 
 class QAConv(Module):
@@ -29,41 +28,41 @@ def __init__(self, num_features, height, width):
         self.height = height
         self.width = width
         self.bn = nn.BatchNorm1d(1)
-        self.fc = nn.Linear(self.height * self.width * 2, 1)
+        self.fc = nn.Linear(self.height * self.width, 1)
         self.logit_bn = nn.BatchNorm1d(1)
         self.kernel = None
+        self.reset_parameters()
 
     def reset_running_stats(self):
         self.bn.reset_running_stats()
         self.logit_bn.reset_running_stats()
 
     def reset_parameters(self):
         self.bn.reset_parameters()
-        self.fc.reset_parameters()
         self.logit_bn.reset_parameters()
+        with torch.no_grad():
+            self.fc.weight.fill_(1. / (self.height * self.width))
 
     def _check_input_dim(self, input):
         if input.dim() != 4:
             raise ValueError('expected 4D input (got {}D input)'.format(input.dim()))
 
     def make_kernel(self, features): # probe features
-        kernel = features.permute([0, 2, 3, 1])  # [p, h, w, d]
-        kernel = kernel.reshape(-1, self.num_features, 1, 1)  # [phw, d, 1, 1]
-        self.kernel = kernel
+        self.kernel = features
 
     def forward(self, features):  # gallery features
         self._check_input_dim(features)
 
         hw = self.height * self.width
         batch_size = features.size(0)
-
-        score = F.conv2d(features, self.kernel)  # [g, phw, h, w]
+        score = torch.einsum('g c h w, p c y x -> g p y x h w', features, self.kernel)
         score = score.view(batch_size, -1, hw, hw)
         score = torch.cat((score.max(dim=2)[0], score.max(dim=3)[0]), dim=-1)
 
-        score = score.view(-1, 1, 2 * hw)
-        score = self.bn(score).view(-1, 2 * hw)
+        score = score.view(-1, 1, hw)
+        score = self.bn(score).view(-1, hw)
         score = self.fc(score)
+        score = score.view(-1, 2).sum(dim=-1, keepdim=True)
         score = self.logit_bn(score)
         score = score.view(batch_size, -1).t() # [p, g]
 

reid/models/resmap.py

@@ -5,8 +5,8 @@
         Shengcai Liao
         scliao@ieee.org
     Version:
-        V1.1
-        Feb. 7, 2021
+        V1.2
+        July 4, 2021
     """
 
 from __future__ import absolute_import
@@ -58,9 +58,8 @@ def __init__(self, depth, ibn_type=None, final_layer='layer3', neck=128, pretrai
             out_planes = fea_dims[final_layer]
 
         if neck > 0:
-            self.neck_conv = nn.Conv2d(out_planes, neck, kernel_size=3, padding=1, bias=False)
+            self.neck_conv = nn.Conv2d(out_planes, neck, kernel_size=3, padding=1)
             out_planes = neck
-            self.neck_bn = nn.BatchNorm2d(out_planes)
 
         self.num_features = out_planes
 
@@ -73,7 +72,6 @@ def forward(self, inputs):
 
         if self.neck > 0:
             x = self.neck_conv(x)
-            x = self.neck_bn(x)
 
         x = F.normalize(x)