initial commit - just template files

utils/module_mixins.py

@@ -0,0 +1,172 @@
+from collections import defaultdict
+
+from abc import ABC
+from argparse import Namespace
+
+import torch
+
+from torch import nn
+from torch.optim import Adam
+from torch.utils.data import DataLoader
+from torchcontrib.optim import SWA
+from torchvision.transforms import Compose
+
+from _templates.new_project.datasets.template_dataset import TemplateDataset
+
+from audio_toolset.audio_io import NormalizeLocal
+from modules.utils import LightningBaseModule
+from utils.transforms import ToTensor
+
+from _templates.new_project.utils.project_config import GlobalVar as GlobalVars
+
+
+class BaseOptimizerMixin:
+
+    def configure_optimizers(self):
+        assert isinstance(self, LightningBaseModule)
+        opt = Adam(params=self.parameters(), lr=self.params.lr, weight_decay=self.params.weight_decay)
+        if self.params.sto_weight_avg:
+            # TODO: Make this glabaly available.
+            opt = SWA(opt, swa_start=10, swa_freq=5, swa_lr=0.05)
+        return opt
+
+    def on_train_end(self):
+        assert isinstance(self, LightningBaseModule)
+        for opt in self.trainer.optimizers:
+            if isinstance(opt, SWA):
+                opt.swap_swa_sgd()
+
+    def on_epoch_end(self):
+        assert isinstance(self, LightningBaseModule)
+        if self.params.opt_reset_interval:
+            if self.current_epoch % self.params.opt_reset_interval == 0:
+                for opt in self.trainer.optimizers:
+                    opt.state = defaultdict(dict)
+
+
+class BaseTrainMixin:
+
+    absolute_loss = nn.L1Loss()
+    nll_loss = nn.NLLLoss()
+    bce_loss = nn.BCELoss()
+
+    def training_step(self, batch_xy, batch_nb, *_, **__):
+        assert isinstance(self, LightningBaseModule)
+        batch_x, batch_y = batch_xy
+        y = self(batch_x).main_out
+        bce_loss = self.bce_loss(y, batch_y)
+        return dict(loss=bce_loss, log=dict(batch_nb=batch_nb))
+
+    def training_epoch_end(self, outputs):
+        assert isinstance(self, LightningBaseModule)
+        keys = list(outputs[0].keys())
+
+        summary_dict = dict(log={f'mean_{key}': torch.mean(torch.stack([output[key]
+                                                                        for output in outputs]))
+                                 for key in keys if 'loss' in key})
+        return summary_dict
+
+
+class BaseValMixin:
+
+    absolute_loss = nn.L1Loss()
+    nll_loss = nn.NLLLoss()
+    bce_loss = nn.BCELoss()
+
+    def validation_step(self, batch_xy, batch_idx, _, *__, **___):
+        assert isinstance(self, LightningBaseModule)
+        batch_x, batch_y = batch_xy
+        y = self(batch_x).main_out
+        val_bce_loss = self.bce_loss(y, batch_y)
+        return dict(val_bce_loss=val_bce_loss,
+                    batch_idx=batch_idx, y=y, batch_y=batch_y)
+
+    def validation_epoch_end(self, outputs, *_, **__):
+        assert isinstance(self, LightningBaseModule)
+        summary_dict = dict(log=dict())
+        # In case of Multiple given dataloader this will outputs will be: list[list[dict[]]]
+        # for output_idx, output in enumerate(outputs):
+        # else:list[dict[]]
+        keys = list(outputs.keys())
+        # Add Every Value das has a "loss" in it, by calc. mean over all occurences.
+        summary_dict['log'].update({f'mean_{key}': torch.mean(torch.stack([output[key]
+                                                                           for output in outputs]))
+                                    for key in keys if 'loss' in key}
+                                   )
+        """
+        # Additional Score like the unweighted Average Recall:
+        # UnweightedAverageRecall
+        y_true = torch.cat([output['batch_y'] for output in outputs]) .cpu().numpy()
+        y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().numpy()
+
+        y_pred = (y_pred >= 0.5).astype(np.float32)
+
+        uar_score = sklearn.metrics.recall_score(y_true, y_pred, labels=[0, 1], average='macro',
+                                                 sample_weight=None, zero_division='warn')
+
+        summary_dict['log'].update({f'uar_score': uar_score})
+        """
+
+        return summary_dict
+
+
+class BinaryMaskDatasetMixin:
+
+    def build_dataset(self):
+        assert isinstance(self, LightningBaseModule)
+
+        # Dataset
+        # =============================================================================
+        # Data Augmentations or Utility Transformations
+
+        transforms = Compose([NormalizeLocal(), ToTensor()])
+
+        # Dataset
+        dataset = Namespace(
+            **dict(
+                # TRAIN DATASET
+                train_dataset=TemplateDataset(self.params.root, setting=GlobalVars.DATA_OPTIONS.train,
+                                              transforms=transforms
+                                              ),
+
+                # VALIDATION DATASET
+                val_dataset=TemplateDataset(self.params.root, setting=GlobalVars.vali,
+                                            ),
+
+                # TEST DATASET
+                test_dataset=TemplateDataset(self.params.root, setting=GlobalVars.test,
+                                             ),
+
+            )
+        )
+        return dataset
+
+
+class BaseDataloadersMixin(ABC):
+
+    # Dataloaders
+    # ================================================================================
+    # Train Dataloader
+    def train_dataloader(self):
+        assert isinstance(self, LightningBaseModule)
+        # In case you want to implement bootstraping
+        # sampler = RandomSampler(self.dataset.train_dataset, True, len(self.dataset.train_dataset))
+        sampler = None
+        return DataLoader(dataset=self.dataset.train_dataset, shuffle=True if not sampler else None, sampler=sampler,
+                          batch_size=self.params.batch_size,
+                          num_workers=self.params.worker)
+
+    # Test Dataloader
+    def test_dataloader(self):
+        assert isinstance(self, LightningBaseModule)
+        return DataLoader(dataset=self.dataset.test_dataset, shuffle=False,
+                          batch_size=self.params.batch_size,
+                          num_workers=self.params.worker)
+
+    # Validation Dataloader
+    def val_dataloader(self):
+        assert isinstance(self, LightningBaseModule)
+        val_dataloader = DataLoader(dataset=self.dataset.val_dataset, shuffle=False,
+                                    batch_size=self.params.batch_size, num_workers=self.params.worker)
+        # Alternative return [val_dataloader, alternative dataloader], there will be a dataloader_idx in validation_step
+        return val_dataloader