From 451f78f820301ddf5771c8a4dd5c019500e670ec Mon Sep 17 00:00:00 2001
From: Si11ium <steffen.illium@ifi.lmu.de>
Date: Mon, 4 May 2020 18:45:13 +0200
Subject: [PATCH] BandwiseBinaryClassifier is work in progress; TODO: Shape
Piping.
---
_paramters.py | 2 +-
datasets/binar_masks.py | 2 +-
main_inference.py | 4 +-
models/bandwise_binary_classifier.py | 97 ++++++++++++++++++++++++++++
models/binary_classifier.py | 67 +++++++++----------
models/module_mixins.py | 55 ++++++++++++++++
util/config.py | 5 +-
7 files changed, 190 insertions(+), 42 deletions(-)
create mode 100644 models/bandwise_binary_classifier.py
create mode 100644 models/module_mixins.py
diff --git a/_paramters.py b/_paramters.py
index 16c2852..b42846d 100644
--- a/_paramters.py
+++ b/_paramters.py
@@ -44,7 +44,7 @@ main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
main_arg_parser.add_argument("--model_bias", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--model_norm", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
+main_arg_parser.add_argument("--model_dropout", type=float, default=0.2, help="")
# Project Parameters
main_arg_parser.add_argument("--project_name", type=str, default=_ROOT.name, help="")
diff --git a/datasets/binar_masks.py b/datasets/binar_masks.py
index 8159ff2..1e77f3d 100644
--- a/datasets/binar_masks.py
+++ b/datasets/binar_masks.py
@@ -30,7 +30,7 @@ class BinaryMasksDataset(Dataset):
self._labels = self._build_labels()
self._wav_folder = self.data_root / 'wav'
self._wav_files = list(sorted(self._labels.keys()))
- self._mel_folder = self.data_root / 'raw_mel'
+ self._mel_folder = self.data_root / 'transformed'
def _build_labels(self):
with open(Path(self.data_root) / 'lab' / 'labels.csv', mode='r') as f:
diff --git a/main_inference.py b/main_inference.py
index 65ef2c2..e617b74 100644
--- a/main_inference.py
+++ b/main_inference.py
@@ -1,7 +1,7 @@
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, ToTensor
-from ml_lib.audio_toolset.audio_io import Melspectogram, NormalizeLocal
+from ml_lib.audio_toolset.audio_io import AudioToMel, NormalizeLocal
# Dataset and Dataloaders
# =============================================================================
@@ -11,7 +11,7 @@ from ml_lib.utils.model_io import SavedLightningModels
from util.config import MConfig
from util.logging import MLogger
-transforms = Compose([Melspectogram(), ToTensor(), NormalizeLocal()])
+transforms = Compose([AudioToMel(), ToTensor(), NormalizeLocal()])
# Datasets
from datasets.binar_masks import BinaryMasksDataset
diff --git a/models/bandwise_binary_classifier.py b/models/bandwise_binary_classifier.py
new file mode 100644
index 0000000..89a2e83
--- /dev/null
+++ b/models/bandwise_binary_classifier.py
@@ -0,0 +1,97 @@
+from argparse import Namespace
+
+from torch import nn
+from torch.nn import ModuleDict
+
+from torchvision.transforms import Compose, ToTensor
+
+from ml_lib.audio_toolset.audio_io import AudioToMel, NormalizeLocal, PowerToDB, MelToImage
+from ml_lib.modules.blocks import ConvModule
+from ml_lib.modules.utils import LightningBaseModule, Flatten, BaseModuleMixin_Dataloaders, HorizontalSplitter, \
+ HorizontalMerger
+from models.module_mixins import BaseOptimizerMixin, BaseTrainMixin, BaseValMixin
+
+
+class BandwiseBinaryClassifier(BaseModuleMixin_Dataloaders,
+ BaseTrainMixin,
+ BaseValMixin,
+ BaseOptimizerMixin,
+ LightningBaseModule
+ ):
+
+ def __init__(self, hparams):
+ super(BandwiseBinaryClassifier, self).__init__(hparams)
+
+ # Dataset and Dataloaders
+ # =============================================================================
+ # Transforms
+ transforms = Compose([AudioToMel(), MelToImage(), ToTensor(), NormalizeLocal()])
+ # Datasets
+ from datasets.binar_masks import BinaryMasksDataset
+ self.dataset = Namespace(
+ **dict(
+ train_dataset=BinaryMasksDataset(self.params.root, setting='train', transforms=transforms),
+ val_dataset=BinaryMasksDataset(self.params.root, setting='devel', transforms=transforms),
+ test_dataset=BinaryMasksDataset(self.params.root, setting='test', transforms=transforms),
+ )
+ )
+
+ # Model Paramters
+ # =============================================================================
+ # Additional parameters
+ self.in_shape = self.dataset.train_dataset.sample_shape
+ self.conv_filters = self.params.filters
+ self.criterion = nn.BCELoss()
+ self.n_band_sections = 5
+
+ # Utility Modules
+ self.split = HorizontalSplitter(self.in_shape, self.n_band_sections)
+
+ # Modules with Parameters
+ modules = {f"conv_1_{band_section}":
+ ConvModule(self.in_shape, self.conv_filters[0], 3, conv_stride=2, **self.params.module_kwargs)
+ for band_section in range(self.n_band_sections)}
+
+ modules.update({f"conv_2_{band_section}":
+ ConvModule(self.conv_1.shape, self.conv_filters[1], 3, conv_stride=2,
+ **self.params.module_kwargs) for band_section in range(self.n_band_sections)}
+ )
+ modules.update({f"conv_3_{band_section}":
+ ConvModule(self.conv_2.shape, self.conv_filters[2], 3, conv_stride=2,
+ **self.params.module_kwargs)
+ for band_section in range(self.n_band_sections)}
+ )
+
+ self.full_1 = nn.Linear(self.flat.shape, self.params.lat_dim, self.params.bias)
+ self.full_2 = nn.Linear(self.full_1.out_features, self.full_1.out_features // 2, self.params.bias)
+
+ self.full_out = nn.Linear(self.full_2.out_features, 1, self.params.bias)
+
+ # Utility Modules
+ self.merge = HorizontalMerger(self.split.shape, self.n_band_sections)
+ self.conv_dict = ModuleDict(modules=modules)
+ self.flat = Flatten(self.conv_3.shape)
+ self.dropout = nn.Dropout2d(self.params.dropout) if self.params.dropout else lambda x: x
+ self.activation = self.params.activation()
+ self.sigmoid = nn.Sigmoid()
+
+ def forward(self, batch, **kwargs):
+ tensors = self.split(batch)
+ for idx, tensor in enumerate(tensors):
+ tensor[idx] = self.conv_dict[f"conv_1_{idx}"](tensor)
+ for idx, tensor in enumerate(tensors):
+ tensor[idx] = self.conv_dict[f"conv_2_{idx}"](tensor)
+ for idx, tensor in enumerate(tensors):
+ tensor[idx] = self.conv_dict[f"conv_3_{idx}"](tensor)
+
+ tensor = self.merge(tensors)
+ tensor = self.flat(tensor)
+ tensor = self.full_1(tensor)
+ tensor = self.activation(tensor)
+ tensor = self.dropout(tensor)
+ tensor = self.full_2(tensor)
+ tensor = self.activation(tensor)
+ tensor = self.dropout(tensor)
+ tensor = self.full_out(tensor)
+ tensor = self.sigmoid(tensor)
+ return tensor
diff --git a/models/binary_classifier.py b/models/binary_classifier.py
index 0d663e1..cbc44fa 100644
--- a/models/binary_classifier.py
+++ b/models/binary_classifier.py
@@ -1,41 +1,21 @@
from argparse import Namespace
-import torch
from torch import nn
-from torch.optim import Adam
+
from torchvision.transforms import Compose, ToTensor
-from ml_lib.audio_toolset.audio_io import Melspectogram, NormalizeLocal
+from ml_lib.audio_toolset.audio_io import AudioToMel, NormalizeLocal, PowerToDB, MelToImage
from ml_lib.modules.blocks import ConvModule
from ml_lib.modules.utils import LightningBaseModule, Flatten, BaseModuleMixin_Dataloaders
+from models.module_mixins import BaseOptimizerMixin, BaseTrainMixin, BaseValMixin
-class BinaryClassifier(BaseModuleMixin_Dataloaders, LightningBaseModule):
-
- @classmethod
- def name(cls):
- return cls.__name__
-
- def configure_optimizers(self):
- return Adam(params=self.parameters(), lr=self.params.lr)
-
- def training_step(self, batch_xy, batch_nb, *args, **kwargs):
- batch_x, batch_y = batch_xy
- y = self(batch_x)
- loss = self.criterion(y, batch_y)
- return dict(loss=loss)
-
- def validation_step(self, batch_xy, batch_idx, *args, **kwargs):
- batch_x, batch_y = batch_xy
- y = self(batch_x)
- val_loss = self.criterion(y, batch_y)
- return dict(val_loss=val_loss, batch_idx=batch_idx)
-
- def validation_epoch_end(self, outputs):
- overall_val_loss = torch.mean(torch.stack([output['val_loss'] for output in outputs]))
- return dict(log=dict(
- mean_val_loss=overall_val_loss)
- )
+class BinaryClassifier(BaseModuleMixin_Dataloaders,
+ BaseTrainMixin,
+ BaseValMixin,
+ BaseOptimizerMixin,
+ LightningBaseModule
+ ):
def __init__(self, hparams):
super(BinaryClassifier, self).__init__(hparams)
@@ -43,7 +23,7 @@ class BinaryClassifier(BaseModuleMixin_Dataloaders, LightningBaseModule):
# Dataset and Dataloaders
# =============================================================================
# Transforms
- transforms = Compose([Melspectogram(), ToTensor(), NormalizeLocal()])
+ transforms = Compose([AudioToMel(), MelToImage(), ToTensor(), NormalizeLocal()])
# Datasets
from datasets.binar_masks import BinaryMasksDataset
self.dataset = Namespace(
@@ -58,29 +38,42 @@ class BinaryClassifier(BaseModuleMixin_Dataloaders, LightningBaseModule):
# =============================================================================
# Additional parameters
self.in_shape = self.dataset.train_dataset.sample_shape
+ self.conv_filters = self.params.filters
self.criterion = nn.BCELoss()
- # Modules
- self.conv_1 = ConvModule(self.in_shape, 32, 3, conv_stride=2, **self.params.module_kwargs)
- self.conv_2 = ConvModule(self.conv_1.shape, 64, 5, conv_stride=2, **self.params.module_kwargs)
- self.conv_3 = ConvModule(self.conv_2.shape, 128, 7, conv_stride=2, **self.params.module_kwargs)
- self.flat = Flatten(self.conv_3.shape)
- self.full_1 = nn.Linear(self.flat.shape, 32, self.params.bias)
+ # Modules with Parameters
+ self.conv_1 = ConvModule(self.in_shape, self.conv_filters[0], 3, conv_stride=2, **self.params.module_kwargs)
+ self.conv_1b = ConvModule(self.conv_1.shape, self.conv_filters[0], 1, conv_stride=1, **self.params.module_kwargs)
+ self.conv_2 = ConvModule(self.conv_1b.shape, self.conv_filters[1], 5, conv_stride=2, **self.params.module_kwargs)
+ self.conv_2b = ConvModule(self.conv_2.shape, self.conv_filters[1], 1, conv_stride=1, **self.params.module_kwargs)
+ self.conv_3 = ConvModule(self.conv_2b.shape, self.conv_filters[2], 7, conv_stride=2, **self.params.module_kwargs)
+ self.conv_3b = ConvModule(self.conv_3.shape, self.conv_filters[2], 1, conv_stride=1, **self.params.module_kwargs)
+
+ self.flat = Flatten(self.conv_3b.shape)
+ self.full_1 = nn.Linear(self.flat.shape, self.params.lat_dim, self.params.bias)
self.full_2 = nn.Linear(self.full_1.out_features, self.full_1.out_features // 2, self.params.bias)
- self.activation = self.params.activation()
self.full_out = nn.Linear(self.full_2.out_features, 1, self.params.bias)
+
+ # Utility Modules
+ self.dropout = nn.Dropout2d(self.params.dropout) if self.params.dropout else lambda x: x
+ self.activation = self.params.activation()
self.sigmoid = nn.Sigmoid()
def forward(self, batch, **kwargs):
tensor = self.conv_1(batch)
+ tensor = self.conv_1b(tensor)
tensor = self.conv_2(tensor)
+ tensor = self.conv_2b(tensor)
tensor = self.conv_3(tensor)
+ tensor = self.conv_3b(tensor)
tensor = self.flat(tensor)
tensor = self.full_1(tensor)
tensor = self.activation(tensor)
+ tensor = self.dropout(tensor)
tensor = self.full_2(tensor)
tensor = self.activation(tensor)
+ tensor = self.dropout(tensor)
tensor = self.full_out(tensor)
tensor = self.sigmoid(tensor)
return tensor
diff --git a/models/module_mixins.py b/models/module_mixins.py
new file mode 100644
index 0000000..df67460
--- /dev/null
+++ b/models/module_mixins.py
@@ -0,0 +1,55 @@
+import sklearn
+import torch
+import numpy as np
+from torch.nn import L1Loss
+from torch.optim import Adam
+
+
+class BaseOptimizerMixin:
+
+ def configure_optimizers(self):
+ return Adam(params=self.parameters(), lr=self.params.lr)
+
+
+class BaseTrainMixin:
+
+ def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+ batch_x, batch_y = batch_xy
+ y = self(batch_x)
+ loss = self.criterion(y, batch_y)
+ return dict(loss=loss)
+
+ def training_epoch_end(self, outputs):
+ mean_train_loss = torch.mean(torch.stack([output['loss'] for output in outputs]))
+ return dict(log=dict(mean_train_loss=mean_train_loss))
+
+
+class BaseValMixin:
+
+ absolute_loss = L1Loss()
+
+ def validation_step(self, batch_xy, batch_idx, *args, **kwargs):
+ batch_x, batch_y = batch_xy
+ y = self(batch_x)
+ val_loss = self.criterion(y, batch_y)
+ absolute_error = self.absolute_loss(y, batch_y)
+ return dict(val_loss=val_loss, absolute_error=absolute_error, batch_idx=batch_idx, y=y, batch_y=batch_y)
+
+ def validation_epoch_end(self, outputs):
+ overall_val_loss = torch.mean(torch.stack([output['val_loss'] for output in outputs]))
+ mean_absolute_error = torch.mean(torch.stack([output['absolute_error'] for output in outputs]))
+
+ # 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')
+
+ return dict(
+ log=dict(mean_val_loss=overall_val_loss,
+ mean_absolute_error=mean_absolute_error,
+ uar_score=uar_score)
+ )
diff --git a/util/config.py b/util/config.py
index b2b6c17..2ec6c47 100644
--- a/util/config.py
+++ b/util/config.py
@@ -1,9 +1,12 @@
from ml_lib.utils.config import Config
from models.binary_classifier import BinaryClassifier
+from models.bandwise_binary_classifier import BandwiseBinaryClassifier
class MConfig(Config):
+ # TODO: There should be a way to automate this.
@property
def _model_map(self):
- return dict(BinaryClassifier=BinaryClassifier)
+ return dict(BinaryClassifier=BinaryClassifier,
+ BandwiseBinaryClassifier=BandwiseBinaryClassifier)