update

liuning-scu-cn · Aug 3, 2019 · 757d528 · 757d528
1 parent ccd1575
commit 757d528
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Showing 17 changed files with 35 additions and 31 deletions.
diff --git a/core/__init__.py b/core/__init__.py
diff --git a/core/config.py b/core/config.py
diff --git a/core/loss.py b/core/loss.py
@@ -172,12 +172,14 @@ def hard_example_mining(dist_mat, labels, return_inds=False):
 
     # `dist_ap` means distance(anchor, positive)
     # both `dist_ap` and `relative_p_inds` with shape [N, 1]
-    dist_ap, relative_p_inds = torch.max(
-        dist_mat[is_pos].contiguous().view(N, -1), 1, keepdim=True)
+    dist_ap, relative_p_inds = torch.max((dist_mat * is_pos.float()).contiguous().view(N, -1), 1, keepdim=True)
+
     # `dist_an` means distance(anchor, negative)
     # both `dist_an` and `relative_n_inds` with shape [N, 1]
-    dist_an, relative_n_inds = torch.min(
-        dist_mat[is_neg].contiguous().view(N, -1), 1, keepdim=True)
+    temp = dist_mat * is_neg.float()
+    temp[temp == 0] = 10e5
+    dist_an, relative_n_inds = torch.min((temp).contiguous().view(N, -1), 1, keepdim=True)
+
     # shape [N]
     dist_ap = dist_ap.squeeze(1)
     dist_an = dist_an.squeeze(1)
@@ -189,9 +191,9 @@ def hard_example_mining(dist_mat, labels, return_inds=False):
                .unsqueeze(0).expand(N, N))
         # shape [N, 1]
         p_inds = torch.gather(
-            ind[is_pos].contiguous().view(N, -1), 1, relative_p_inds.data)
+            (ind * is_pos.long()).contiguous().view(N, -1), 1, relative_p_inds.data)
         n_inds = torch.gather(
-            ind[is_neg].contiguous().view(N, -1), 1, relative_n_inds.data)
+            (ind * is_pos.long()).contiguous().view(N, -1), 1, relative_n_inds.data)
         # shape [N]
         p_inds = p_inds.squeeze(1)
         n_inds = n_inds.squeeze(1)

diff --git a/core/model.py b/core/model.py
@@ -561,7 +561,7 @@ def get_loss(self, logits, labels, direction):
 
         loss = self.loss(logits[0], labels) + self.loss(logits[1], direction)
         triplet_loss = global_loss(TripletLoss(margin=0.3), logits[2], labels)[0]
-        return loss + triplet_loss
+        return triplet_loss + loss
 
     def features(self, x):
         # backbone
@@ -589,9 +589,7 @@ def forward(self, x, label=None, direction=None):
         x = self.fc7(x)
         x = l2_norm(x)
 
-        # TigerID
         glogit = self.cls(x)
-        # Left/Right
         dlogit = self.cls_direction(x)
 
         return [glogit, dlogit, x]
@@ -628,6 +626,9 @@ def __init__(self, classes=107):
         self.cls_direction = nn.Linear(512, 2)
         self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
 
+    def fix_params(self, is_training=True):
+        pass
+
     def get_loss(self, logits, labels, direction):
         loss = self.loss(logits[0], labels) + self.loss(logits[1], direction)
         return loss
@@ -690,6 +691,9 @@ def __init__(self, classes=107):
         self.cls_direction = nn.Linear(512, 2)
         self.loss = LabelSmoothingCrossEntropy(smoothing=0.1)
 
+    def fix_params(self, is_training=True):
+        pass
+
     def get_loss(self, logits, labels, direction):
         loss = self.loss(logits[0], labels) + self.loss(logits[1], direction)
         return loss

diff --git a/core/re_ranking.py b/core/re_ranking.py
diff --git a/core/schedule.py b/core/schedule.py
diff --git a/core/seed.py b/core/seed.py
diff --git a/core/step_lr.py b/core/step_lr.py
diff --git a/core/utils.py b/core/utils.py
diff --git a/dataload/__init__.py b/dataload/__init__.py
diff --git a/dataload/constant.py b/dataload/constant.py
diff --git a/dataload/dataloader.py b/dataload/dataloader.py
diff --git a/dataload/mydataset.py b/dataload/mydataset.py
diff --git a/finetune_tiger_cnn5_triplet.py b/finetune_tiger_cnn5_triplet.py
@@ -29,7 +29,7 @@ def main():
     if os.path.exists(save_dir):
         raise NameError('model dir exists!')
     os.makedirs(save_dir)
-    copyfile('./finetune_triplet.py.py', save_dir + '/train.py')
+    copyfile('./finetune_tiger_cnn5_triplet.py', save_dir + '/train.py')
     copyfile('./core/model.py', save_dir + '/model.py')
     copyfile('./core/config.py', save_dir + '/config.py')
     logging = init_log(save_dir)
@@ -39,16 +39,17 @@ def main():
     gallery_paths = ['./datalist/gallery.txt', ]
     probe_paths = ['./datalist/probe.txt', ]
 
-    train_iter, gallery_iter, probe_iter = load_direction_gallery_probe(
+    train_iter, gallery_iter, probe_iter = load_triplet_direction_gallery_probe(
         root='./database',
         train_paths=train_paths,
         gallery_paths=gallery_paths,
         probe_paths=probe_paths,
         signal=' ',
         resize_size=RESIZE_SIZE,
         input_size=INPUT_SIZE,
-        batch_size=BATCH_SIZE,
-        num_workers=2
+        batch_size=8,
+        num_workers=2,
+        collate_fn=train_collate
     )
 
     feature_size = 1024
@@ -67,7 +68,7 @@ def main():
 
     exp_lr_scheduler = StepLRScheduler(optimizer=optimizer, decay_t=20, decay_rate=0.1, warmup_lr_init=1e-5, warmup_t=3)
 
-    net.load_state_dict(torch.load('./model/tiger_cnn1/model.ckpt'))
+    net.load_state_dict(torch.load('./model/tiger_cnn1/model.ckpt')['net_state_dict'])
     # net.fix_params(is_training=False)
     net = net.cuda()
     if multi_gpus:
@@ -108,6 +109,7 @@ def main():
             optimizer.zero_grad()
 
             logits = net(inputs, labels)
+
             if multi_gpus:
                 loss = net.module.get_loss(logits, labels, direction)
             else:
@@ -231,4 +233,3 @@ def main():
     main()
 
 
-
diff --git a/finetune_tiger_cnn8_triplet.py b/finetune_tiger_cnn8_triplet.py
@@ -20,7 +20,7 @@
 init_environment()
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 multi_gpus = False
-model_name = 'tiger_cnn5'
+model_name = 'tiger_cnn8'
 
 
 def main():
@@ -29,7 +29,7 @@ def main():
     if os.path.exists(save_dir):
         raise NameError('model dir exists!')
     os.makedirs(save_dir)
-    copyfile('./finetune_triplet.py.py', save_dir + '/train.py')
+    copyfile('./finetune_tiger_cnn8_triplet.py', save_dir + '/train.py')
     copyfile('./core/model.py', save_dir + '/model.py')
     copyfile('./core/config.py', save_dir + '/config.py')
     logging = init_log(save_dir)
@@ -39,16 +39,17 @@ def main():
     gallery_paths = ['./datalist/gallery.txt', ]
     probe_paths = ['./datalist/probe.txt', ]
 
-    train_iter, gallery_iter, probe_iter = load_direction_gallery_probe(
+    train_iter, gallery_iter, probe_iter = load_triplet_direction_gallery_probe(
         root='./database',
         train_paths=train_paths,
         gallery_paths=gallery_paths,
         probe_paths=probe_paths,
         signal=' ',
         resize_size=RESIZE_SIZE,
         input_size=INPUT_SIZE,
-        batch_size=BATCH_SIZE,
-        num_workers=2
+        batch_size=8,
+        num_workers=2,
+        collate_fn=train_collate
     )
 
     feature_size = 1024
@@ -66,7 +67,7 @@ def main():
     )
     exp_lr_scheduler = StepLRScheduler(optimizer=optimizer, decay_t=20, decay_rate=0.1, warmup_lr_init=1e-5, warmup_t=3)
 
-    net.load_state_dict(torch.load('./model/tiger_cnn3/model.ckpt'))
+    net.load_state_dict(torch.load('./model/tiger_cnn3/model.ckpt')['net_state_dict'])
     net.fix_params(is_training=False)
     net = net.cuda()
     if multi_gpus:

diff --git a/test.py b/test.py
@@ -122,8 +122,8 @@ def main():
     )
 
     feature_size = 1024
-    net = tiger_cnn8(classes=107)
-    net.load_state_dict(torch.load('/media/liuning/UBUNTU 16_0/ValidateTiger/model/SEResNet50_0.3TripletTigerCNN3_Finetuning_WarmUp_Direction_288_448_Gallery2_CutOut_RandomErase_FlipTest_0.001lr_8batchsize_20190802_200847/iter07_model.ckpt')['net_state_dict'])
+    net = tiger_cnn1(classes=107)
+    net.load_state_dict(torch.load('./model/tiger_cnn1/model.ckpt')['net_state_dict'])
     net = net.cuda()
 
     # val

diff --git a/train.py b/train.py
@@ -20,7 +20,7 @@
 init_environment()
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 multi_gpus = False
-model_name = 'tiger_cnn1'
+model_name = 'tiger_cnn7'
 
 
 def main():
@@ -95,13 +95,6 @@ def main():
         ignore_params += list(map(id, net.fuse_fc7.parameters()))
         base_params = filter(lambda p: id(p) not in ignore_params, net.parameters())
         extra_params = filter(lambda p: id(p) in ignore_params, net.parameters())
-    elif model_name == 'tiger_cnn5':
-        net = tiger_cnn5(classes=107)
-        ignore_params = list(map(id, net.cls.parameters()))
-        ignore_params += list(map(id, net.cls_direction.parameters()))
-        ignore_params += list(map(id, net.fc7.parameters()))
-        base_params = filter(lambda p: id(p) not in ignore_params, net.parameters())
-        extra_params = filter(lambda p: id(p) in ignore_params, net.parameters())
     elif model_name == 'tiger_cnn6':
         net = tiger_cnn6(classes=107)
         ignore_params = list(map(id, net.cls.parameters()))
@@ -116,6 +109,9 @@ def main():
         ignore_params += list(map(id, net.fc7.parameters()))
         base_params = filter(lambda p: id(p) not in ignore_params, net.parameters())
         extra_params = filter(lambda p: id(p) in ignore_params, net.parameters())
+    else:
+        _print('error!!!!!! please input correct model name.')
+        return
 
     optimizer = optim.SGD(
         [{'params': base_params, 'lr': 0.001},