Upload bevformer_tiny_test.py with huggingface_hub

bevformer_tiny_test.py

@@ -22,7 +22,7 @@ custom_hooks = [
     dict(
         by_epoch=False,
         clean_local=False,
-        interval=5,
+        interval=1,
         repo_id='5421Project',
         type='CheckpointUploader'),
     dict(repo_id='5421Project', resume_type='last', type='CheckpointResumer'),
@@ -331,7 +331,7 @@ decoder = dict(
 default_hooks = dict(
     checkpoint=dict(
         by_epoch=False,
-        interval=5,
+        interval=1,
         max_keep_ckpts=1,
         save_best=[
             'loss',
@@ -340,7 +340,7 @@ default_hooks = dict(
         ],
         type='CheckpointHookV2'),
     logger=dict(
-        interval=2,
+        interval=1,
         interval_exp_name=1000,
         log_metric_by_epoch=False,
         type='LoggerHook'),
@@ -419,10 +419,10 @@ input_modality = dict(
     use_lidar=False,
     use_map=False,
     use_radar=False)
-interval = 5
+interval = 1
 launcher = 'none'
 load_from = None
-log_interval = 2
+log_interval = 1
 log_processor = dict(window_size=20)
 lr_config = dict(
     min_lr_ratio=0.001,
@@ -431,7 +431,7 @@ lr_config = dict(
     warmup_iters=500,
     warmup_ratio=0.3333333333333333)
 max_epochs = 5
-max_iters = 10
+max_iters = 2
 model = dict(
     img_backbone=dict(
         depth=50,
@@ -929,7 +929,7 @@ test_pipeline = [
         ],
         type='MultiScaleFlipAug3D'),
 ]
-train_cfg = dict(by_epoch=False, max_epochs=5, max_iters=10, val_interval=5)
+train_cfg = dict(by_epoch=False, max_epochs=5, max_iters=2, val_interval=1)
 train_dataloader = dict(
     batch_size=1,
     collate_fn=dict(type='train_collate'),
@@ -1302,7 +1302,7 @@ val_evaluator = dict(metrics=[
         type='src.NuScenesMetric',
         version='v1.0-mini'),
 ])
-val_interval = 5
+val_interval = 1
 val_max_iters = 1
 version = 'v1.0-mini'
 visualizer = dict(