| """Native SSL class loading extracted from the fully validated preparation code.""" |
|
|
| import ast |
| import os |
| import pickle |
|
|
| def native_ssl_class(resources): |
| import abc |
| import gc |
| import importlib.util |
| import typing |
| import lightning.pytorch as pl |
| import numpy as np |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as functional |
| from torchmetrics import ExplainedVariance, MeanSquaredError, MetricCollection |
| base_file = resources / 'ssl-source/base.py' |
| spec = importlib.util.spec_from_file_location('pinned_ssl_base', base_file) |
| base = importlib.util.module_from_spec(spec) |
| spec.loader.exec_module(base) |
| autoencoder_file = resources / 'ssl-source/cellnet_autoencoder.py' |
| parsed = ast.parse(autoencoder_file.read_text()) |
| classes = {node.name: node for node in parsed.body if isinstance(node, ast.ClassDef)} |
| namespace = {'__name__': 'pinned_ssl_autoencoder', '__file__': str(autoencoder_file), 'abc': abc, 'gc': gc, 'os': os, 'pickle': pickle, 'np': np, 'torch': torch, 'nn': nn, 'F': functional, 'pl': pl, 'MLP': base.MLP, 'ExplainedVariance': ExplainedVariance, 'MeanSquaredError': MeanSquaredError, 'MetricCollection': MetricCollection} |
| namespace.update({name: getattr(typing, name) for name in ('Callable', 'Dict', 'List', 'Optional', 'Tuple')}) |
| executable = ast.Module(body=[classes['BaseAutoEncoder'], classes['MLPAutoEncoder']], type_ignores=[]) |
| exec(compile(executable, str(autoencoder_file), 'exec'), namespace) |
| return namespace['MLPAutoEncoder'] |
|
|
