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stage_1_get_ref_image.py

@@ -111,7 +111,6 @@ for i in range(0, len(meta_channels)):
 
     heartbeat_ts: torch.Tensor = bandpass(
         data=data[..., i],
-        device=data.device,
         low_frequency=config["lower_freqency_bandpass"],
         high_frequency=config["upper_freqency_bandpass"],
         fs=sample_frequency,

stage_4_process.py

@@ -12,7 +12,6 @@ from functions.load_config import load_config
 from functions.get_experiments import get_experiments
 from functions.get_trials import get_trials
 from functions.binning import binning
-from functions.ImageAlignment import ImageAlignment
 from functions.align_refref import align_refref
 from functions.perform_donor_volume_rotation import perform_donor_volume_rotation
 from functions.perform_donor_volume_translation import perform_donor_volume_translation
@@ -127,7 +126,9 @@ def process_trial(
 
     mylogger.info(f"Loading ref file data: {ref_image_path_acceptor}")
     ref_image_acceptor: torch.Tensor = torch.tensor(
-        np.load(ref_image_path_acceptor).astype(dtype_np), dtype=dtype, device=device
+        np.load(ref_image_path_acceptor).astype(dtype_np),
+        dtype=dtype,
+        device=data.device,
     )
 
     ref_image_path_donor: str = os.path.join(ref_image_path, "donor.npy")
@@ -138,7 +139,7 @@ def process_trial(
 
     mylogger.info(f"Loading ref file data: {ref_image_path_donor}")
     ref_image_donor: torch.Tensor = torch.tensor(
-        np.load(ref_image_path_donor).astype(dtype_np), dtype=dtype, device=device
+        np.load(ref_image_path_donor).astype(dtype_np), dtype=dtype, device=data.device
     )
 
     ref_image_path_oxygenation: str = os.path.join(ref_image_path, "oxygenation.npy")
@@ -149,7 +150,9 @@ def process_trial(
 
     mylogger.info(f"Loading ref file data: {ref_image_path_oxygenation}")
     ref_image_oxygenation: torch.Tensor = torch.tensor(
-        np.load(ref_image_path_oxygenation).astype(dtype_np), dtype=dtype, device=device
+        np.load(ref_image_path_oxygenation).astype(dtype_np),
+        dtype=dtype,
+        device=data.device,
     )
 
     ref_image_path_volume: str = os.path.join(ref_image_path, "volume.npy")
@@ -160,7 +163,7 @@ def process_trial(
 
     mylogger.info(f"Loading ref file data: {ref_image_path_volume}")
     ref_image_volume: torch.Tensor = torch.tensor(
-        np.load(ref_image_path_volume).astype(dtype_np), dtype=dtype, device=device
+        np.load(ref_image_path_volume).astype(dtype_np), dtype=dtype, device=data.device
     )
 
     refined_mask_file: str = os.path.join(ref_image_path, "mask_not_rotated.npy")
@@ -171,7 +174,7 @@ def process_trial(
 
     mylogger.info(f"Loading mask file data: {refined_mask_file}")
     mask: torch.Tensor = torch.tensor(
-        np.load(refined_mask_file).astype(dtype_np), dtype=dtype, device=device
+        np.load(refined_mask_file).astype(dtype_np), dtype=dtype, device=data.device
     )
     mylogger.info("-==- Done -==-")
 
@@ -190,7 +193,7 @@ def process_trial(
             kernel_size=int(config["binning_kernel_size"]),
             stride=int(config["binning_stride"]),
             divisor_override=int(config["binning_divisor_override"]),
-        ).to(device=device)
+        ).to(device=data.device)
         ref_image_acceptor = (
             binning(
                 ref_image_acceptor.unsqueeze(-1).unsqueeze(-1),
@@ -245,8 +248,6 @@ def process_trial(
         mylogger.info("-==- Done -==-")
 
     mylogger.info("Preparing alignment")
-    image_alignment = ImageAlignment(default_dtype=dtype, device=device)
-
     mylogger.info("Re-order Raw data")
     data = data.moveaxis(-2, 0).moveaxis(-1, 0)
     mylogger.info(f"Data shape: {data.shape}")
@@ -260,7 +261,6 @@ def process_trial(
         mylogger=mylogger,
         ref_image_acceptor=ref_image_acceptor,
         ref_image_donor=ref_image_donor,
-        image_alignment=image_alignment,
         batch_size=config["alignment_batch_size"],
         fill_value=-100.0,
     )
@@ -372,7 +372,6 @@ def process_trial(
         volume=data[volume_index, ...],
         ref_image_donor=ref_image_donor,
         ref_image_volume=ref_image_volume,
-        image_alignment=image_alignment,
         batch_size=config["alignment_batch_size"],
         fill_value=-100.0,
         config=config,
@@ -409,7 +408,6 @@ def process_trial(
         volume=data[volume_index, ...],
         ref_image_donor=ref_image_donor,
         ref_image_volume=ref_image_volume,
-        image_alignment=image_alignment,
         batch_size=config["alignment_batch_size"],
         fill_value=-100.0,
         config=config,
@@ -474,7 +472,6 @@ def process_trial(
     mylogger.info("Extract heartbeat from volume signal")
     heartbeat_ts: torch.Tensor = bandpass(
         data=data[volume_index, ...].movedim(0, -1).clone(),
-        device=data.device,
         low_frequency=config["lower_freqency_bandpass"],
         high_frequency=config["upper_freqency_bandpass"],
         fs=sample_frequency,
@@ -488,7 +485,16 @@ def process_trial(
     heartbeat_ts = heartbeat_ts.movedim(0, -1)
     heartbeat_ts -= heartbeat_ts.mean(dim=0, keepdim=True)
 
-    volume_heartbeat, _, _ = torch.linalg.svd(heartbeat_ts, full_matrices=False)
+    try:
+        volume_heartbeat, _, _ = torch.linalg.svd(heartbeat_ts, full_matrices=False)
+    except torch.cuda.OutOfMemoryError:
+        mylogger.info("torch.cuda.OutOfMemoryError: Fallback to cpu")
+        volume_heartbeat_cpu, _, _ = torch.linalg.svd(
+            heartbeat_ts.cpu(), full_matrices=False
+        )
+        volume_heartbeat = volume_heartbeat_cpu.to(heartbeat_ts.data, copy=True)
+        del volume_heartbeat_cpu
+
     volume_heartbeat = volume_heartbeat[:, 0]
     volume_heartbeat -= volume_heartbeat[
         config["skip_frames_in_the_beginning"] :
@@ -525,7 +531,6 @@ def process_trial(
     for i in range(0, data.shape[0]):
         y = bandpass(
             data=data[i, ...].movedim(0, -1).clone(),
-            device=data.device,
             low_frequency=config["lower_freqency_bandpass"],
             high_frequency=config["upper_freqency_bandpass"],
             fs=sample_frequency,
@@ -909,10 +914,21 @@ for experiment_counter in range(0, experiments.shape[0]):
         )
         mylogger.info("")
 
-        process_trial(
-            config=config,
-            mylogger=mylogger,
-            experiment_id=experiment_id,
-            trial_id=trial_id,
-            device=device,
-        )
+        try:
+            process_trial(
+                config=config,
+                mylogger=mylogger,
+                experiment_id=experiment_id,
+                trial_id=trial_id,
+                device=device,
+            )
+        except torch.cuda.OutOfMemoryError:
+            mylogger.info("WARNING: RUNNING IN FAILBACK MODE!!!!")
+            mylogger.info("Not enough GPU memory. Retry on CPU")
+            process_trial(
+                config=config,
+                mylogger=mylogger,
+                experiment_id=experiment_id,
+                trial_id=trial_id,
+                device=torch.device("cpu"),
+            )