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from sunlab.common import ShapeDataset
from sunlab.common import MaxAbsScaler
def process_and_load_dataset(
dataset_file, model_folder, magnification=10, scaler=MaxAbsScaler
):
"""# Load a dataset and process a models' Latent Space on the Dataset"""
raise NotImplemented("Still Implementing for PyTorch")
from ..models import load_aae
from sunlab.common import import_full_dataset
model = load_aae(model_folder, normalization_scaler=scaler)
dataset = import_full_dataset(
dataset_file, magnification=magnification, scaler=model.scaler
)
latent = model.encoder(dataset.dataset).numpy()
assert len(latent.shape) == 2, "Only 1D Latent Vectors Supported"
for dim in range(latent.shape[1]):
dataset.dataframe[f"Latent-{dim}"] = latent[:, dim]
return dataset
def process_and_load_datasets(
dataset_file_list, model_folder, magnification=10, scaler=MaxAbsScaler
):
from pandas import concat
from ..models import load_aae
raise NotImplemented("Still Implementing for PyTorch")
dataframes = []
datasets = []
for dataset_file in dataset_file_list:
dataset = process_and_load_dataset(
dataset_file, model_folder, magnification, scaler
)
model = load_aae(model_folder, normalization_scaler=scaler)
dataframe = dataset.dataframe
for label in ["ActinEdge", "Filopodia", "Bleb", "Lamellipodia"]:
if label in dataframe.columns:
dataframe[label.lower()] = dataframe[label]
if label.lower() not in dataframe.columns:
dataframe[label.lower()] = 0
latent_columns = [f"Latent-{dim}" for dim in range(model.latent_size)]
datasets.append(dataset)
dataframes.append(
dataframe[
dataset.data_columns
+ dataset.label_columns
+ latent_columns
+ ["Frames", "CellNum"]
+ ["actinedge", "filopodia", "bleb", "lamellipodia"]
]
)
return datasets, concat(dataframes)
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