diff --git a/_modules/torchreid/engine/engine.html b/_modules/torchreid/engine/engine.html index 7f190c9..6d6f22a 100644 --- a/_modules/torchreid/engine/engine.html +++ b/_modules/torchreid/engine/engine.html @@ -537,10 +537,10 @@ end = time.time() features = self.extract_features(imgs) batch_time.update(time.time() - end) - features = features.cpu().clone() + features = features.cpu() f_.append(features) - pids_.extend(pids) - camids_.extend(camids) + pids_.extend(pids.tolist()) + camids_.extend(camids.tolist()) f_ = torch.cat(f_, 0) pids_ = np.asarray(pids_) camids_ = np.asarray(camids_) diff --git a/index.html b/index.html index df1411d..8bfacef 100644 --- a/index.html +++ b/index.html @@ -256,22 +256,22 @@ python setup.py develop

  • Load data manager

    • datamanager = torchreid.data.ImageDataManager(
-    root='reid-data',
-    sources='market1501',
-    targets='market1501',
+    root="reid-data",
+    sources="market1501",
+    targets="market1501",
     height=256,
     width=128,
     batch_size_train=32,
     batch_size_test=100,
-    transforms=['random_flip', 'random_crop']
+    transforms=["random_flip", "random_crop"]
 )

    3 Build model, optimizer and lr_scheduler

    model = torchreid.models.build_model(
-    name='resnet50',
+    name="resnet50",
     num_classes=datamanager.num_train_pids,
-    loss='softmax',
+    loss="softmax",
     pretrained=True
 )
 
@@ -279,13 +279,13 @@ python setup.py develop
 
 optimizer = torchreid.optim.build_optimizer(
     model,
-    optim='adam',
+    optim="adam",
     lr=0.0003
 )
 
 scheduler = torchreid.optim.build_lr_scheduler(
     optimizer,
-    lr_scheduler='single_step',
+    lr_scheduler="single_step",
     stepsize=20
 )
    @@ -306,7 +306,7 @@ python setup.py develop

  • Run training and test

    • engine.run(
-    save_dir='log/resnet50',
+    save_dir="log/resnet50",
     max_epoch=60,
     eval_freq=10,
     print_freq=10,