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David Rotermund
GitHub
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2 changed files with 100 additions and 17 deletions
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@ -11,6 +11,18 @@
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"oxygenation",
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"volume"
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],
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"classical_ratio_mode": false, // true: a/d false: 1+a-d
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// Regression
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"target_camera_acceptor": "acceptor",
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"regressor_cameras_acceptor": [
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"oxygenation",
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"volume"
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],
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"target_camera_donor": "donor",
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"regressor_cameras_donor": [
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"oxygenation",
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"volume"
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],
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// binning
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"binning_enable": true,
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"binning_before_alignment": false, // otherwise at the end after everything else
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@ -120,7 +120,7 @@ def process_trial(
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free_mem: int = cuda_total_memory - max(
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[torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
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)
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mylogger.info(f"CUDA memory before loading RAW data: {free_mem//1024} MByte")
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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data_np: np.ndarray = np.load(filename_data, mmap_mode="r").astype(dtype_np)
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data: torch.Tensor = torch.zeros(data_np.shape, dtype=dtype, device=device)
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@ -133,12 +133,24 @@ def process_trial(
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free_mem = cuda_total_memory - max(
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[torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
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)
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mylogger.info(f"CUDA memory after loading RAW data: {free_mem//1024} MByte")
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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del data_np
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mylogger.info(f"Data shape: {data.shape}")
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mylogger.info("-==- Done -==-")
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mylogger.info("Finding limit values in the RAW data and mark them for masking")
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limit: float = (2**16) - 1
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for i in range(0, data.shape[3]):
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zero_pixel_mask: torch.Tensor = torch.any(data[..., i] >= limit, dim=-1)
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data[zero_pixel_mask, :, i] = -100.0
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mylogger.info(
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f"{meta_channels[i]}: "
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f"found {int(zero_pixel_mask.type(dtype=dtype).sum())} pixel "
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f"with limit values "
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)
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mylogger.info("-==- Done -==-")
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mylogger.info("Reference images and mask")
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ref_image_path: str = config["ref_image_path"]
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@ -281,7 +293,7 @@ def process_trial(
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ref_image_donor=ref_image_donor,
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image_alignment=image_alignment,
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batch_size=config["alignment_batch_size"],
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fill_value=-1.0,
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fill_value=-100.0,
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)
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mylogger.info(f"Rotation: {round(float(angle_refref[0]),2)} degree")
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mylogger.info(
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@ -308,7 +320,7 @@ def process_trial(
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=-1.0,
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fill=-100.0,
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)
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ref_image_oxygenation = tv.transforms.functional.affine(
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@ -318,7 +330,7 @@ def process_trial(
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=-1.0,
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fill=-100.0,
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).squeeze(0)
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mylogger.info("-==- Done -==-")
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@ -336,7 +348,7 @@ def process_trial(
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=-1.0,
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fill=-100.0,
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)
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mylogger.info("Translate acceptor")
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@ -347,7 +359,7 @@ def process_trial(
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=-1.0,
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fill=-100.0,
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)
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mylogger.info("Rotate oxygenation")
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@ -358,7 +370,7 @@ def process_trial(
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=-1.0,
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fill=-100.0,
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)
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mylogger.info("Translate oxygenation")
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@ -369,7 +381,7 @@ def process_trial(
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scale=1.0,
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shear=0,
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interpolation=tv.transforms.InterpolationMode.BILINEAR,
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fill=-1.0,
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fill=-100.0,
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)
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mylogger.info("-==- Done -==-")
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@ -392,7 +404,7 @@ def process_trial(
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ref_image_volume=ref_image_volume,
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image_alignment=image_alignment,
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batch_size=config["alignment_batch_size"],
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fill_value=-1.0,
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fill_value=-100.0,
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)
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mylogger.info(
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@ -427,7 +439,7 @@ def process_trial(
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ref_image_volume=ref_image_volume,
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image_alignment=image_alignment,
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batch_size=config["alignment_batch_size"],
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fill_value=-1.0,
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fill_value=-100.0,
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)
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mylogger.info(
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@ -450,6 +462,17 @@ def process_trial(
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np.save(temp_path, tvec_donor_volume.cpu())
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mylogger.info("-==- Done -==-")
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mylogger.info("Finding zeros values in the RAW data and mark them for masking")
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for i in range(0, data.shape[0]):
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zero_pixel_mask = torch.any(data[i, ...] == 0, dim=0)
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data[i, :, zero_pixel_mask] = -100.0
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mylogger.info(
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f"{config['required_order'][i]}: "
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f"found {int(zero_pixel_mask.type(dtype=dtype).sum())} pixel "
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f"with zeros "
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)
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mylogger.info("-==- Done -==-")
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mylogger.info("Update mask with the new regions due to alignment")
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new_mask_area: torch.Tensor = torch.any(torch.any(data < -0.1, dim=0), dim=0).bool()
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@ -497,6 +520,14 @@ def process_trial(
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del heartbeat_ts
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if device != torch.device("cpu"):
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torch.cuda.empty_cache()
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mylogger.info("Empty CUDA cache")
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free_mem = cuda_total_memory - max(
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[torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
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)
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_volume_heartbeat.npy"
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)
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@ -551,6 +582,14 @@ def process_trial(
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acceptor_heartbeat_factor = heartbeat_coefficients[acceptor_index, ...].clone()
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del heartbeat_coefficients
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if device != torch.device("cpu"):
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torch.cuda.empty_cache()
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mylogger.info("Empty CUDA cache")
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free_mem = cuda_total_memory - max(
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[torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
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)
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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mylogger.info("Calculate scaling factor for donor and acceptor")
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donor_factor: torch.Tensor = (
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donor_heartbeat_factor + acceptor_heartbeat_factor
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@ -636,12 +675,12 @@ def process_trial(
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mylogger.info("creating a copy of the data")
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data_filtered = data.clone().movedim(1, -1)
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if device != torch.device("cpu"):
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torch.cuda.empty_cache()
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mylogger.info("Empty CUDA cache")
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free_mem = cuda_total_memory - max(
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[torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
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)
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mylogger.info(
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f"CUDA memory after reserving RAM for data_filtered: {free_mem//1024} MByte"
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)
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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overwrite_fft_gauss: None | torch.Tensor = None
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for i in range(0, data_filtered.shape[0]):
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@ -670,17 +709,49 @@ def process_trial(
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mylogger.info("data_filtered is populated")
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if device != torch.device("cpu"):
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torch.cuda.empty_cache()
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mylogger.info("Empty CUDA cache")
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free_mem = cuda_total_memory - max(
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[torch.cuda.memory_reserved(device), torch.cuda.memory_allocated(device)]
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)
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mylogger.info(
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f"CUDA memory after data_filtered is populated: {free_mem//1024} MByte"
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)
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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mylogger.info("-==- Done -==-")
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exit()
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# if config["classical_ratio_mode"]:
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# ratio_sequence: torch.Tensor = data_acceptor / data_donor
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# ratio_sequence /= ratio_sequence.mean(dim=-1, keepdim=True)
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# else:
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# ratio_sequence: torch.Tensor = 1 + data_acceptor - data_donor
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# ratio_sequence = torch.nan_to_num(ratio_sequence, nan=0.0)
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# if config["binning_enable"] and (config["binning_before_alignment"] is False):
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# mylogger.info("Binning of data")
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# mylogger.info(
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# (
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# f"kernel_size={int(config['binning_kernel_size'])},"
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# f"stride={int(config['binning_stride'])},"
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# f"divisor_override={int(config['binning_divisor_override'])}"
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# )
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# )
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# # dims... ?
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# ratio_sequence = binning(
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# ratio_sequence,
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# kernel_size=int(config["binning_kernel_size"]),
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# stride=int(config["binning_stride"]),
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# divisor_override=int(config["binning_divisor_override"]),
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# )
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# # dims... ?
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# mask = binning(
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# mask,
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# kernel_size=int(config["binning_kernel_size"]),
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# stride=int(config["binning_stride"]),
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# divisor_override=int(config["binning_divisor_override"]),
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# )
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# save mask und ratio_sequence
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return
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