Parameter Adjustmens and Ensemble Model Implementation

models/ensemble.py

@@ -0,0 +1,55 @@
+from argparse import Namespace
+from pathlib import Path
+
+import torch
+from torch import nn
+from torch.nn import ModuleList
+
+from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.utils.config import Config
+from ml_lib.utils.model_io import SavedLightningModels
+from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetFunction,
+                                BaseDataloadersMixin)
+
+
+class Ensemble(BinaryMaskDatasetFunction,
+                     BaseDataloadersMixin,
+                     BaseTrainMixin,
+                     BaseValMixin,
+                     BaseOptimizerMixin,
+                     LightningBaseModule
+                     ):
+
+    def __init__(self, hparams):
+        super(Ensemble, self).__init__(hparams)
+
+        # Dataset
+        # =============================================================================
+        self.dataset = self.build_dataset()
+
+        # Model Paramters
+        # =============================================================================
+        # Additional parameters
+        self.in_shape = self.dataset.train_dataset.sample_shape
+        self.conv_filters = self.params.filters
+        self.criterion = nn.BCELoss()
+
+        # Pre_trained_models
+        out_path = Path('output') / self.params.secondary_type
+        # exp_paths = list(out_path.rglob(f'*{self.params.exp_fingerprint}'))
+        exp_paths = list(out_path.rglob('*e87b8f455ba134504b1ae17114ac2a2a'))
+        config_ini_files = sum([list(exp_path.rglob('config.ini')) for exp_path in exp_paths], [])
+
+        self.model_list = ModuleList()
+
+        configs = [Config() for _ in range(len(config_ini_files))]
+        for config, ini_file in zip(configs, config_ini_files):
+            config.read_file(ini_file.open('r'))
+            model = SavedLightningModels.load_checkpoint(models_root_path=config.exp_path / config.version).restore()
+            self.model_list.append(model)
+
+    def forward(self, batch, **kwargs):
+        ys = [model(batch).main_out for model in self.model_list]
+        tensor = torch.stack(ys).mean(dim=0)
+
+        return Namespace(main_out=tensor)