From 7850807bdd2b7bb75efc5ba18c334029136716ac Mon Sep 17 00:00:00 2001
From: tydlwav <michael.mao99@gmail.com>
Date: Thu, 11 Jul 2019 20:48:05 -0400
Subject: [PATCH 1/4] add mean distance for same bag

---
 scripts/main.py            |  6 ++++--
 torchreid/engine/engine.py |  5 +++--
 torchreid/utils/rerank.py  | 23 ++++++++++++++++++++++-
 3 files changed, 29 insertions(+), 5 deletions(-)

diff --git a/scripts/main.py b/scripts/main.py
index a5c90a6..be89ccc 100755
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -60,7 +60,8 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 scheduler=scheduler,
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
-                experiment=experiment
+                experiment=experiment,
+                by_id="mean"
             )
         else:
             engine = torchreid.engine.ImageTripletEngine(
@@ -73,7 +74,8 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 scheduler=scheduler,
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
-                experiment=experiment
+                experiment=experiment,
+                by_id="mean"
             )
     
     else:
diff --git a/torchreid/engine/engine.py b/torchreid/engine/engine.py
index 39cd683..2350a09 100644
--- a/torchreid/engine/engine.py
+++ b/torchreid/engine/engine.py
@@ -32,7 +32,7 @@ class Engine(object):
         use_cpu (bool, optional): use cpu. Default is False.
     """
 
-    def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=False, experiment=None):
+    def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=False, experiment=None, by_id="mean"):
         self.datamanager = datamanager
         self.model = model
         self.optimizer = optimizer
@@ -40,6 +40,7 @@ def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=F
         self.use_gpu = (torch.cuda.is_available() and not use_cpu)
         self.writer = None
         self.experiment = experiment
+        self.by_id = by_id
 
         # check attributes
         if not isinstance(self.model, nn.Module):
@@ -311,7 +312,7 @@ def _evaluate(self, arch, epoch, dataset_name='', queryloader=None, galleryloade
 
                 print('Computing distance matrix with metric={} ...'.format(dist_metric))
                 distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
-                distmat = distmat.numpy()
+                distmat = distmat_by_id(distmat.numpy(), q_pids, g_pids, self.by_id)
 
                 if rerank:
                     print('Applying person re-ranking ...')
diff --git a/torchreid/utils/rerank.py b/torchreid/utils/rerank.py
index dfe82ed..f75dbad 100644
--- a/torchreid/utils/rerank.py
+++ b/torchreid/utils/rerank.py
@@ -26,7 +26,7 @@
 from __future__ import print_function
 from __future__ import division
 
-__all__ = ['re_ranking']
+__all__ = ['re_ranking', 'distmat_by_id']
 
 import numpy as np
 
@@ -98,3 +98,24 @@ def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
     del jaccard_dist
     final_dist = final_dist[:query_num,query_num:]
     return final_dist
+
+def distmat_by_id(distmat, g_id, by_id):
+    """
+    transforms distance matrix to 
+    """
+    if by_id == None:
+        return distmat
+    elif by_id == "mean":
+        unique_ids = set(g_id)
+        gid_distmat = np.empty((distmat.shape[0], len(unique_ids)))
+        for bag_id, i in enumerate(unique_ids):
+            gid_distmat[:, i] = np.mean(distmat[:, np.asarray(g_id) == bag_id], axis=-1, keepdims=True)
+        return gid_distmat
+    elif by_id == "self_attention":
+        # TODO: self attention
+        return distmat
+    elif by_id == "multi_head_attention":
+        # TODO: multi-headed attention
+        return distmat
+    else:
+        raise ValueError('Must be valid by_id method')
\ No newline at end of file

From 0f1c2f0ff344ede3a662f123745a0a4b3db71664 Mon Sep 17 00:00:00 2001
From: tydlwav <michael.mao99@gmail.com>
Date: Thu, 11 Jul 2019 22:14:52 -0400
Subject: [PATCH 2/4] some bad changes

---
 scripts/main.py                   | 3 ++-
 torchreid/engine/engine.py        | 4 ++--
 torchreid/engine/image/softmax.py | 2 +-
 torchreid/engine/image/triplet.py | 2 +-
 torchreid/utils/__init__.py       | 2 +-
 torchreid/utils/rerank.py         | 6 +++---
 6 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/scripts/main.py b/scripts/main.py
index be89ccc..3097c3d 100755
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -53,6 +53,7 @@ def build_datamanager(args):
 def build_engine(args, datamanager, model, optimizer, scheduler, experiment=experiment):
     if args.app == 'image':
         if args.loss == 'softmax':
+            print('\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n')
             engine = torchreid.engine.ImageSoftmaxEngine(
                 datamanager,
                 model,
@@ -61,7 +62,7 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
                 experiment=experiment,
-                by_id="mean"
+                by_id=None
             )
         else:
             engine = torchreid.engine.ImageTripletEngine(
diff --git a/torchreid/engine/engine.py b/torchreid/engine/engine.py
index 2350a09..01480fe 100644
--- a/torchreid/engine/engine.py
+++ b/torchreid/engine/engine.py
@@ -13,7 +13,7 @@
 from torch.nn import functional as F
 
 import torchreid
-from torchreid.utils import AverageMeter, visualize_ranked_results, visualize_cam, save_checkpoint, re_ranking
+from torchreid.utils import AverageMeter, visualize_ranked_results, visualize_cam, save_checkpoint, re_ranking, distmat_by_id
 from torchreid.losses import DeepSupervision
 from torchreid import metrics
 
@@ -312,7 +312,7 @@ def _evaluate(self, arch, epoch, dataset_name='', queryloader=None, galleryloade
 
                 print('Computing distance matrix with metric={} ...'.format(dist_metric))
                 distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
-                distmat = distmat_by_id(distmat.numpy(), q_pids, g_pids, self.by_id)
+                distmat = distmat_by_id(distmat.numpy(), g_pids, self.by_id)
 
                 if rerank:
                     print('Applying person re-ranking ...')
diff --git a/torchreid/engine/image/softmax.py b/torchreid/engine/image/softmax.py
index 03fe0c9..1e4faa0 100644
--- a/torchreid/engine/image/softmax.py
+++ b/torchreid/engine/image/softmax.py
@@ -63,7 +63,7 @@ class ImageSoftmaxEngine(engine.Engine):
     """
 
     def __init__(self, datamanager, model, optimizer, scheduler=None, use_cpu=False,
-                 label_smooth=True, experiment=None):
+                 label_smooth=True, experiment=None, by_id="mean"):
         super(ImageSoftmaxEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment)
         
         self.criterion = CrossEntropyLoss(
diff --git a/torchreid/engine/image/triplet.py b/torchreid/engine/image/triplet.py
index 2d0c4fd..b8c809c 100644
--- a/torchreid/engine/image/triplet.py
+++ b/torchreid/engine/image/triplet.py
@@ -70,7 +70,7 @@ class ImageTripletEngine(engine.Engine):
 
     def __init__(self, datamanager, model, optimizer, margin=0.3,
                  weight_t=1, weight_x=1, scheduler=None, use_cpu=False,
-                 label_smooth=True, experiment=None):
+                 label_smooth=True, experiment=None, by_id="mean"):
         super(ImageTripletEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment)
 
         self.weight_t = weight_t
diff --git a/torchreid/utils/__init__.py b/torchreid/utils/__init__.py
index fb04b6e..8fedf9b 100644
--- a/torchreid/utils/__init__.py
+++ b/torchreid/utils/__init__.py
@@ -6,5 +6,5 @@
 from .tools import *
 from .reidtools import *
 from .torchtools import *
-from .rerank import re_ranking
+from .rerank import re_ranking, distmat_by_id
 from .model_complexity import compute_model_complexity
diff --git a/torchreid/utils/rerank.py b/torchreid/utils/rerank.py
index f75dbad..3a853d7 100644
--- a/torchreid/utils/rerank.py
+++ b/torchreid/utils/rerank.py
@@ -103,13 +103,13 @@ def distmat_by_id(distmat, g_id, by_id):
     """
     transforms distance matrix to 
     """
-    if by_id == None:
+    if by_id is None:
         return distmat
     elif by_id == "mean":
         unique_ids = set(g_id)
         gid_distmat = np.empty((distmat.shape[0], len(unique_ids)))
-        for bag_id, i in enumerate(unique_ids):
-            gid_distmat[:, i] = np.mean(distmat[:, np.asarray(g_id) == bag_id], axis=-1, keepdims=True)
+        for i, bag_id in enumerate(unique_ids):
+            gid_distmat[:, i] = np.mean(distmat[:, np.asarray(g_id) == bag_id], axis=-1)
         return gid_distmat
     elif by_id == "self_attention":
         # TODO: self attention

From 05c3f9d1fe123340d09a9b37e68738ff8760469c Mon Sep 17 00:00:00 2001
From: tydlwav <michael.mao99@gmail.com>
Date: Thu, 11 Jul 2019 23:03:14 -0400
Subject: [PATCH 3/4] working reranking

---
 scripts/main.py                   |  5 ++---
 torchreid/engine/engine.py        |  9 +++++----
 torchreid/engine/image/softmax.py |  4 ++--
 torchreid/engine/image/triplet.py |  4 ++--
 torchreid/utils/__init__.py       |  3 ++-
 torchreid/utils/multi_image.py    | 27 +++++++++++++++++++++++++++
 torchreid/utils/rerank.py         | 23 +----------------------
 7 files changed, 41 insertions(+), 34 deletions(-)
 create mode 100644 torchreid/utils/multi_image.py

diff --git a/scripts/main.py b/scripts/main.py
index 3097c3d..7709812 100755
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -53,7 +53,6 @@ def build_datamanager(args):
 def build_engine(args, datamanager, model, optimizer, scheduler, experiment=experiment):
     if args.app == 'image':
         if args.loss == 'softmax':
-            print('\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n')
             engine = torchreid.engine.ImageSoftmaxEngine(
                 datamanager,
                 model,
@@ -62,7 +61,7 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
                 experiment=experiment,
-                by_id=None
+                combine_method="mean"
             )
         else:
             engine = torchreid.engine.ImageTripletEngine(
@@ -76,7 +75,7 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
                 experiment=experiment,
-                by_id="mean"
+                combine_method="mean"
             )
     
     else:
diff --git a/torchreid/engine/engine.py b/torchreid/engine/engine.py
index 01480fe..04b29d7 100644
--- a/torchreid/engine/engine.py
+++ b/torchreid/engine/engine.py
@@ -13,7 +13,7 @@
 from torch.nn import functional as F
 
 import torchreid
-from torchreid.utils import AverageMeter, visualize_ranked_results, visualize_cam, save_checkpoint, re_ranking, distmat_by_id
+from torchreid.utils import AverageMeter, visualize_ranked_results, visualize_cam, save_checkpoint, re_ranking, combine_by_id
 from torchreid.losses import DeepSupervision
 from torchreid import metrics
 
@@ -32,7 +32,7 @@ class Engine(object):
         use_cpu (bool, optional): use cpu. Default is False.
     """
 
-    def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=False, experiment=None, by_id="mean"):
+    def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=False, experiment=None, combine_method="mean"):
         self.datamanager = datamanager
         self.model = model
         self.optimizer = optimizer
@@ -40,7 +40,7 @@ def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=F
         self.use_gpu = (torch.cuda.is_available() and not use_cpu)
         self.writer = None
         self.experiment = experiment
-        self.by_id = by_id
+        self.combine_method = combine_method
 
         # check attributes
         if not isinstance(self.model, nn.Module):
@@ -301,6 +301,7 @@ def _evaluate(self, arch, epoch, dataset_name='', queryloader=None, galleryloade
                 gf = torch.cat(gf, 0)
                 g_pids = np.asarray(g_pids)
                 g_camids = np.asarray(g_camids)
+                gf, g_pids = combine_by_id(gf, g_pids, self.combine_method)
                 print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
 
                 print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
@@ -312,7 +313,7 @@ def _evaluate(self, arch, epoch, dataset_name='', queryloader=None, galleryloade
 
                 print('Computing distance matrix with metric={} ...'.format(dist_metric))
                 distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
-                distmat = distmat_by_id(distmat.numpy(), g_pids, self.by_id)
+                distmat = distmat.numpy()
 
                 if rerank:
                     print('Applying person re-ranking ...')
diff --git a/torchreid/engine/image/softmax.py b/torchreid/engine/image/softmax.py
index 1e4faa0..facaacc 100644
--- a/torchreid/engine/image/softmax.py
+++ b/torchreid/engine/image/softmax.py
@@ -63,8 +63,8 @@ class ImageSoftmaxEngine(engine.Engine):
     """
 
     def __init__(self, datamanager, model, optimizer, scheduler=None, use_cpu=False,
-                 label_smooth=True, experiment=None, by_id="mean"):
-        super(ImageSoftmaxEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment)
+                 label_smooth=True, experiment=None, combine_method="mean"):
+        super(ImageSoftmaxEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment, combine_method)
         
         self.criterion = CrossEntropyLoss(
             num_classes=self.datamanager.num_train_pids,
diff --git a/torchreid/engine/image/triplet.py b/torchreid/engine/image/triplet.py
index b8c809c..406f378 100644
--- a/torchreid/engine/image/triplet.py
+++ b/torchreid/engine/image/triplet.py
@@ -70,8 +70,8 @@ class ImageTripletEngine(engine.Engine):
 
     def __init__(self, datamanager, model, optimizer, margin=0.3,
                  weight_t=1, weight_x=1, scheduler=None, use_cpu=False,
-                 label_smooth=True, experiment=None, by_id="mean"):
-        super(ImageTripletEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment)
+                 label_smooth=True, experiment=None, combine_method="mean"):
+        super(ImageTripletEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment, combine_method)
 
         self.weight_t = weight_t
         self.weight_x = weight_x
diff --git a/torchreid/utils/__init__.py b/torchreid/utils/__init__.py
index 8fedf9b..01e6769 100644
--- a/torchreid/utils/__init__.py
+++ b/torchreid/utils/__init__.py
@@ -6,5 +6,6 @@
 from .tools import *
 from .reidtools import *
 from .torchtools import *
-from .rerank import re_ranking, distmat_by_id
+from .rerank import re_ranking
+from .multi_image import combine_by_id
 from .model_complexity import compute_model_complexity
diff --git a/torchreid/utils/multi_image.py b/torchreid/utils/multi_image.py
new file mode 100644
index 0000000..709e5c6
--- /dev/null
+++ b/torchreid/utils/multi_image.py
@@ -0,0 +1,27 @@
+import numpy as np
+__all__ = ['combine_by_id']
+
+def combine_by_id(gf, g_pids, method):
+    """
+    transforms features of same bag to a bag embedding
+    """
+    if method is None:
+        return gf, g_pids
+    elif method == "mean":
+        gf = gf.numpy()
+        unique_ids = set(g_pids)
+        new_g_pids = []
+        gf_by_id = np.empty((len(unique_ids), gf.shape[-1]))
+        for i, gid in enumerate(unique_ids):
+            gf_by_id[i] = np.mean(gf[np.asarray(g_pids) == gid], axis=0)
+            new_g_pids.append(gid)
+        gf = torch.tensor(gf_by_id, dtype=torch.float)
+        g_pids = np.array(new_g_pids)
+    elif method == "self_attention":
+        # TODO: self attention
+        return distmat
+    elif method == "multi_head_attention":
+        # TODO: multi-headed attention
+        return distmat
+    else:
+        raise ValueError('Must be valid by_id method')
\ No newline at end of file
diff --git a/torchreid/utils/rerank.py b/torchreid/utils/rerank.py
index 3a853d7..dfe82ed 100644
--- a/torchreid/utils/rerank.py
+++ b/torchreid/utils/rerank.py
@@ -26,7 +26,7 @@
 from __future__ import print_function
 from __future__ import division
 
-__all__ = ['re_ranking', 'distmat_by_id']
+__all__ = ['re_ranking']
 
 import numpy as np
 
@@ -98,24 +98,3 @@ def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
     del jaccard_dist
     final_dist = final_dist[:query_num,query_num:]
     return final_dist
-
-def distmat_by_id(distmat, g_id, by_id):
-    """
-    transforms distance matrix to 
-    """
-    if by_id is None:
-        return distmat
-    elif by_id == "mean":
-        unique_ids = set(g_id)
-        gid_distmat = np.empty((distmat.shape[0], len(unique_ids)))
-        for i, bag_id in enumerate(unique_ids):
-            gid_distmat[:, i] = np.mean(distmat[:, np.asarray(g_id) == bag_id], axis=-1)
-        return gid_distmat
-    elif by_id == "self_attention":
-        # TODO: self attention
-        return distmat
-    elif by_id == "multi_head_attention":
-        # TODO: multi-headed attention
-        return distmat
-    else:
-        raise ValueError('Must be valid by_id method')
\ No newline at end of file

From e1b9a8bdaee5009794dd79cf83e13fcaace4d2c9 Mon Sep 17 00:00:00 2001
From: tydlwav <michael.mao99@gmail.com>
Date: Thu, 11 Jul 2019 23:32:05 -0400
Subject: [PATCH 4/4] adds argument parsing

---
 scripts/default_parser.py      |  2 ++
 scripts/main.py                |  4 ++--
 torchreid/engine/engine.py     | 11 +++++++++++
 torchreid/utils/multi_image.py |  8 ++++++--
 4 files changed, 21 insertions(+), 4 deletions(-)

diff --git a/scripts/default_parser.py b/scripts/default_parser.py
index df8007d..6796c39 100644
--- a/scripts/default_parser.py
+++ b/scripts/default_parser.py
@@ -168,6 +168,8 @@ def init_parser():
                         help='cmc ranks')
     parser.add_argument('--rerank', action='store_true',
                         help='use person re-ranking (by Zhong et al. CVPR2017)')
+    parser.add_argument('--combine-method', type=str, default='none', choices=["none", "mean"],
+                        help='use combine method of [none | mean]')
 
     parser.add_argument('--visrank', action='store_true',
                         help='visualize ranked results, only available in evaluation mode')
diff --git a/scripts/main.py b/scripts/main.py
index 7709812..defef76 100755
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -61,7 +61,7 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
                 experiment=experiment,
-                combine_method="mean"
+                combine_method=args.combine_method
             )
         else:
             engine = torchreid.engine.ImageTripletEngine(
@@ -75,7 +75,7 @@ def build_engine(args, datamanager, model, optimizer, scheduler, experiment=expe
                 use_cpu=args.use_cpu,
                 label_smooth=args.label_smooth,
                 experiment=experiment,
-                combine_method="mean"
+                combine_method=args.combine_method
             )
     
     else:
diff --git a/torchreid/engine/engine.py b/torchreid/engine/engine.py
index 04b29d7..3fc6f4c 100644
--- a/torchreid/engine/engine.py
+++ b/torchreid/engine/engine.py
@@ -301,6 +301,17 @@ def _evaluate(self, arch, epoch, dataset_name='', queryloader=None, galleryloade
                 gf = torch.cat(gf, 0)
                 g_pids = np.asarray(g_pids)
                 g_camids = np.asarray(g_camids)
+
+                # gf = gf.numpy()
+                # unique_ids = set(g_pids)
+                # new_g_pids = []
+                # gf_by_id = np.empty((len(unique_ids), gf.shape[-1]))
+                # for i, gid in enumerate(unique_ids):
+                #     gf_by_id[i] = np.mean(gf[np.asarray(g_pids) == gid], axis=0)
+                #     new_g_pids.append(gid)
+                # gf = torch.tensor(gf_by_id, dtype=torch.float)
+                # g_pids = np.array(new_g_pids)
+
                 gf, g_pids = combine_by_id(gf, g_pids, self.combine_method)
                 print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
 
diff --git a/torchreid/utils/multi_image.py b/torchreid/utils/multi_image.py
index 709e5c6..0c04b38 100644
--- a/torchreid/utils/multi_image.py
+++ b/torchreid/utils/multi_image.py
@@ -1,13 +1,16 @@
 import numpy as np
+import torch
 __all__ = ['combine_by_id']
 
 def combine_by_id(gf, g_pids, method):
     """
     transforms features of same bag to a bag embedding
     """
-    if method is None:
+    if method == "none":
+        print("Does not combine by id")
         return gf, g_pids
     elif method == "mean":
+        print("Calculating mean by id ...")
         gf = gf.numpy()
         unique_ids = set(g_pids)
         new_g_pids = []
@@ -17,6 +20,7 @@ def combine_by_id(gf, g_pids, method):
             new_g_pids.append(gid)
         gf = torch.tensor(gf_by_id, dtype=torch.float)
         g_pids = np.array(new_g_pids)
+        return gf, g_pids
     elif method == "self_attention":
         # TODO: self attention
         return distmat
@@ -24,4 +28,4 @@ def combine_by_id(gf, g_pids, method):
         # TODO: multi-headed attention
         return distmat
     else:
-        raise ValueError('Must be valid by_id method')
\ No newline at end of file
+        raise ValueError('Must be valid combine-method')