Migrate from wandb to tensorboard

applications/contrastive_phenotyping/demo_fit.py

@@ -0,0 +1,45 @@
+from lightning.pytorch import Trainer
+from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.loggers import TensorBoardLogger
+from lightning.pytorch.callbacks import DeviceStatsMonitor
+
+
+from viscy.data.triplet import TripletDataModule
+from viscy.light.engine import ContrastiveModule
+
+
+def main():
+    dm = TripletDataModule(
+        data_path="/hpc/projects/virtual_staining/2024_02_04_A549_DENV_ZIKV_timelapse/registered_chunked.zarr",
+        tracks_path="/hpc/projects/intracellular_dashboard/viral-sensor/2024_02_04_A549_DENV_ZIKV_timelapse/7.1-seg_track/tracking_v1.zarr",
+        source_channel=["Phase3D", "RFP"],
+        z_range=(20, 35),
+        batch_size=16,
+        num_workers=10,
+        initial_yx_patch_size=(384, 384),
+        final_yx_patch_size=(224, 224),
+    )
+    model = ContrastiveModule(
+        backbone="convnext_tiny",
+        in_channels=2,
+        log_batches_per_epoch=2,
+        log_samples_per_batch=3,
+    )
+    trainer = Trainer(
+        max_epochs=5,
+        limit_train_batches=10,
+        limit_val_batches=5,
+        logger=TensorBoardLogger(
+            "/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/test_tb",
+            log_graph=True,
+            default_hp_metric=True,
+        ),
+        log_every_n_steps=1,
+        callbacks=[ModelCheckpoint()],
+        profiler="simple",  # other options: "advanced" uses cprofiler, "pytorch" uses pytorch profiler.
+    )
+    trainer.fit(model, dm)
+
+
+if __name__ == "__main__":
+    main()

tests/preprocessing/test_pixel_ratio.py

@@ -6,10 +6,8 @@
 def test_sematic_class_weights(small_hcs_dataset):
     weights = sematic_class_weights(small_hcs_dataset, "GFP")
     assert weights.shape == (3,)
-    assert_allclose(weights[0], 1.0)
+    assert_allclose(weights[0], 1.0, atol=1e-5)
     # infinity
     assert weights[1] > 1.0
     assert weights[2] > 1.0
-    assert sematic_class_weights(
-        small_hcs_dataset, "GFP", num_classes=2
-    ).shape == (2,)
+    assert sematic_class_weights(small_hcs_dataset, "GFP", num_classes=2).shape == (2,)

viscy/data/hcs.py

@@ -88,9 +88,11 @@ def _read_norm_meta(fov: Position) -> NormMeta | None:
     for channel, channel_values in norm_meta.items():
         for level, level_values in channel_values.items():
             for stat, value in level_values.items():
-                norm_meta[channel][level][stat] = torch.tensor(
-                    value, dtype=torch.float32
-                )
+                if isinstance(value, Tensor):
+                    value = value.clone().float()
+                else:
+                    value = torch.tensor(value, dtype=torch.float32)
+                norm_meta[channel][level][stat] = value
     return norm_meta