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David Rotermund
GitHub
parent
dfa2beae76
commit
37d72264e5
2 changed files with 150 additions and 62 deletions
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@ -708,68 +708,77 @@ def process_trial(
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mylogger.info("Move time dimensions of data to the last dimension")
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data = data.movedim(1, -1)
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mylogger.info("Regression Acceptor")
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mylogger.info(f"Target: {config['target_camera_acceptor']}")
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mylogger.info(
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f"Regressors: constant, linear and {config['regressor_cameras_acceptor']}"
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)
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target_id: int = config["required_order"].index(config["target_camera_acceptor"])
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regressor_id: list[int] = []
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for i in range(0, len(config["regressor_cameras_acceptor"])):
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regressor_id.append(
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config["required_order"].index(config["regressor_cameras_acceptor"][i])
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dual_signal_mode: bool = True
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if len(config["target_camera_acceptor"]) > 0:
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mylogger.info("Regression Acceptor")
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mylogger.info(f"Target: {config['target_camera_acceptor']}")
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mylogger.info(
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f"Regressors: constant, linear and {config['regressor_cameras_acceptor']}"
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)
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target_id: int = config["required_order"].index(
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config["target_camera_acceptor"]
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)
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regressor_id: list[int] = []
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for i in range(0, len(config["regressor_cameras_acceptor"])):
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regressor_id.append(
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config["required_order"].index(config["regressor_cameras_acceptor"][i])
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)
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data_acceptor, coefficients_acceptor = regression(
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mylogger=mylogger,
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target_camera_id=target_id,
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regressor_camera_ids=regressor_id,
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mask=mask_negative,
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data=data,
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data_filtered=data_filtered,
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first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
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)
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data_acceptor, coefficients_acceptor = regression(
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mylogger=mylogger,
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target_camera_id=target_id,
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regressor_camera_ids=regressor_id,
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mask=mask_negative,
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data=data,
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data_filtered=data_filtered,
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first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
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)
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if config["save_regression_coefficients"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_coefficients_acceptor.npy"
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)
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mylogger.info(f"Save acceptor coefficients to {temp_path}")
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np.save(temp_path, coefficients_acceptor.cpu())
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del coefficients_acceptor
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if config["save_regression_coefficients"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_coefficients_acceptor.npy"
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mylogger.info("-==- Done -==-")
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else:
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dual_signal_mode = False
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if len(config["target_camera_donor"]) > 0:
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mylogger.info("Regression Donor")
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mylogger.info(f"Target: {config['target_camera_donor']}")
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mylogger.info(
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f"Regressors: constant, linear and {config['regressor_cameras_donor']}"
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)
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mylogger.info(f"Save acceptor coefficients to {temp_path}")
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np.save(temp_path, coefficients_acceptor.cpu())
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del coefficients_acceptor
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target_id = config["required_order"].index(config["target_camera_donor"])
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regressor_id = []
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for i in range(0, len(config["regressor_cameras_donor"])):
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regressor_id.append(
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config["required_order"].index(config["regressor_cameras_donor"][i])
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)
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mylogger.info("-==- Done -==-")
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mylogger.info("Regression Donor")
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mylogger.info(f"Target: {config['target_camera_donor']}")
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mylogger.info(
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f"Regressors: constant, linear and {config['regressor_cameras_donor']}"
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)
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target_id = config["required_order"].index(config["target_camera_donor"])
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regressor_id = []
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for i in range(0, len(config["regressor_cameras_donor"])):
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regressor_id.append(
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config["required_order"].index(config["regressor_cameras_donor"][i])
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data_donor, coefficients_donor = regression(
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mylogger=mylogger,
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target_camera_id=target_id,
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regressor_camera_ids=regressor_id,
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mask=mask_negative,
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data=data,
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data_filtered=data_filtered,
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first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
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)
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data_donor, coefficients_donor = regression(
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mylogger=mylogger,
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target_camera_id=target_id,
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regressor_camera_ids=regressor_id,
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mask=mask_negative,
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data=data,
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data_filtered=data_filtered,
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first_none_ramp_frame=int(config["skip_frames_in_the_beginning"]),
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)
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if config["save_regression_coefficients"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_coefficients_donor.npy"
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)
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mylogger.info(f"Save acceptor donor to {temp_path}")
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np.save(temp_path, coefficients_donor.cpu())
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del coefficients_donor
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mylogger.info("-==- Done -==-")
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if config["save_regression_coefficients"]:
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temp_path = os.path.join(
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config["export_path"], experiment_name + "_coefficients_donor.npy"
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)
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mylogger.info(f"Save acceptor donor to {temp_path}")
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np.save(temp_path, coefficients_donor.cpu())
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del coefficients_donor
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mylogger.info("-==- Done -==-")
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else:
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dual_signal_mode = False
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del data
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del data_filtered
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@ -783,14 +792,21 @@ def process_trial(
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mylogger.info(f"CUDA memory: {free_mem//1024} MByte")
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mylogger.info("Calculate ratio sequence")
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if config["classical_ratio_mode"]:
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mylogger.info("via acceptor / donor")
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ratio_sequence: torch.Tensor = data_acceptor / data_donor
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mylogger.info("via / mean over time")
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ratio_sequence /= ratio_sequence.mean(dim=-1, keepdim=True)
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if dual_signal_mode:
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if config["classical_ratio_mode"]:
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mylogger.info("via acceptor / donor")
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ratio_sequence: torch.Tensor = data_acceptor / data_donor
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mylogger.info("via / mean over time")
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ratio_sequence /= ratio_sequence.mean(dim=-1, keepdim=True)
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else:
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mylogger.info("via 1.0 + acceptor - donor")
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ratio_sequence = 1.0 + data_acceptor - data_donor
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else:
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mylogger.info("via 1.0 + acceptor - donor")
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ratio_sequence = 1.0 + data_acceptor - data_donor
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mylogger.info("mono signal model")
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if len(config["target_camera_donor"]) > 0:
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ratio_sequence = data_donor.clone()
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else:
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ratio_sequence = data_acceptor.clone()
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mylogger.info("Remove nan")
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ratio_sequence = torch.nan_to_num(ratio_sequence, nan=0.0)
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@ -884,6 +900,15 @@ if (config["save_as_python"] is False) and (config["save_as_matlab"] is False):
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mylogger.info("ERROR: STOP!!!")
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exit()
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if (len(config["target_camera_donor"]) == 0) and (
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len(config["target_camera_acceptor"]) == 0
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):
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mylogger.info(
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"Configure at least target_camera_donor or target_camera_acceptor correctly."
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)
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mylogger.info("ERROR: STOP!!!")
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exit()
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device = get_torch_device(mylogger, config["force_to_cpu"])
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mylogger.info(f"Create directory {config['export_path']} in the case it does not exist")
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