Add linear and no scheduler option

1 files changed, 23 insertions, 9 deletions

diff --git a/supervised/baseline.py b/supervised/baseline.py
index 92d8d30..8a1b567 100644
--- a/supervised/baseline.py
+++ b/supervised/baseline.py
@@ -10,11 +10,11 @@ from torchvision.transforms import transforms, InterpolationMode
 from tqdm import tqdm
 
 from optimizers import LARS
-from schedulers import LinearWarmupAndCosineAnneal
+from schedulers import LinearWarmupAndCosineAnneal, LinearLR
 from supervised.datautils import color_distortion
 from supervised.models import CIFAR10ResNet50
 
-CODENAME = 'cifar10-resnet50-aug-lars-sched'
+CODENAME = 'cifar10-resnet50-aug-lars-warmup'
 DATASET_ROOT = 'dataset'
 TENSORBOARD_PATH = os.path.join('runs', CODENAME)
 CHECKPOINT_PATH = os.path.join('checkpoints', CODENAME)
@@ -27,6 +27,7 @@ N_WORKERS = 2
 SEED = 0
 
 OPTIM = 'lars'
+SCHED = 'warmup-anneal'
 LR = 1
 MOMENTUM = 0.9
 WEIGHT_DECAY = 1e-6
@@ -100,12 +101,24 @@ if RESTORE_EPOCH > 0:
           f'Train loss: {checkpoint["train_loss"]:.4f}\t'
           f'Test loss: {checkpoint["test_loss"]:.4f}')
 
-scheduler = LinearWarmupAndCosineAnneal(
-    optimizer,
-    WARMUP_EPOCHS / N_EPOCHS,
-    N_EPOCHS * num_train_batches,
-    last_epoch=RESTORE_EPOCH * num_train_batches - 1
-)
+if SCHED == 'warmup-anneal':
+    scheduler = LinearWarmupAndCosineAnneal(
+        optimizer,
+        warm_up=WARMUP_EPOCHS / N_EPOCHS,
+        T_max=N_EPOCHS * num_train_batches,
+        last_epoch=RESTORE_EPOCH * num_train_batches - 1
+    )
+elif SCHED == 'linear':
+    scheduler = LinearLR(
+        optimizer,
+        num_epochs=N_EPOCHS * num_train_batches,
+        last_epoch=RESTORE_EPOCH * num_train_batches - 1
+    )
+elif SCHED is None or SCHED == '' or SCHED == 'const':
+    scheduler = None
+else:
+    raise NotImplementedError(f"Scheduler '{SCHED}' is not implemented.")
+
 if OPTIM == 'lars':
     optimizer = LARS(optimizer)
 
@@ -130,7 +143,8 @@ for epoch in range(RESTORE_EPOCH, N_EPOCHS):
         loss = criterion(output, targets)
         loss.backward()
         optimizer.step()
-        scheduler.step()
+        if SCHED:
+            scheduler.step()
 
         train_loss += loss.item()
         train_loss_mean = train_loss / (batch + 1)