GMM clustering and fixed compute pca

applications/contrastive_phenotyping/demo_fit.py

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+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="resnet50",
+        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()