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Audio Dataset

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Si11ium
2020-12-01 16:37:16 +01:00
parent 95561acc35
commit 95dcf22f3d
15 changed files with 468 additions and 145 deletions
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4
models/bandwise_conv_classifier.py
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@@ -5,11 +5,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.util import (LightningBaseModule, Splitter, Merger)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin)
class BandwiseConvClassifier(BinaryMaskDatasetMixin,
class BandwiseConvClassifier(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

4
models/bandwise_conv_multihead_classifier.py
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@@ -6,11 +6,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.util import (LightningBaseModule, Splitter)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin)
class BandwiseConvMultiheadClassifier(BinaryMaskDatasetMixin,
class BandwiseConvMultiheadClassifier(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

4
models/conv_classifier.py
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@@ -5,11 +5,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.util import LightningBaseModule
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin)
class ConvClassifier(BinaryMaskDatasetMixin,
class ConvClassifier(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

4
models/ensemble.py
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@@ -8,11 +8,11 @@ from torch.nn import ModuleList
from ml_lib.modules.util 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, BinaryMaskDatasetMixin,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin)
class Ensemble(BinaryMaskDatasetMixin,
class Ensemble(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

4
models/residual_conv_classifier.py
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@@ -5,11 +5,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule, ResidualModule
from ml_lib.modules.util import LightningBaseModule
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin)
class ResidualConvClassifier(BinaryMaskDatasetMixin,
class ResidualConvClassifier(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

7
models/transformer_model.py
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@@ -9,15 +9,16 @@ from einops import rearrange, repeat
from ml_lib.modules.blocks import TransformerModule
from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin, BaseTestMixin)
MIN_NUM_PATCHES = 16
class VisualTransformer(BinaryMaskDatasetMixin,
class VisualTransformer(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
BaseTestMixin,
BaseOptimizerMixin,
LightningBaseModule
):

111
models/transformer_model_horizontal.py Normal file
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@@ -0,0 +1,111 @@
from argparse import Namespace
import warnings
import torch
from torch import nn
from ml_lib.modules.blocks import TransformerModule
from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin, BaseTestMixin)
MIN_NUM_PATCHES = 16
class HorizontalVisualTransformer(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
BaseTestMixin,
BaseOptimizerMixin,
LightningBaseModule
):
def __init__(self, hparams):
super(HorizontalVisualTransformer, self).__init__(hparams)
# Dataset
# =============================================================================
self.dataset = self.build_dataset()
self.in_shape = self.dataset.train_dataset.sample_shape
assert len(self.in_shape) == 3, 'There need to be three Dimensions'
channels, height, width = self.in_shape
# Model Paramters
# =============================================================================
# Additional parameters
self.embed_dim = self.params.embedding_size
self.patch_size = self.params.patch_size
self.height = height
self.width = width
self.channels = channels
self.new_height = ((self.height - self.patch_size)//1) + 1
num_patches = self.new_height - (self.patch_size // 2)
patch_dim = channels * self.patch_size * self.width
assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
f'attention. Try decreasing your patch size'
# Correct the Embedding Dim
if not self.embed_dim % self.params.heads == 0:
self.embed_dim = (self.embed_dim // self.params.heads) * self.params.heads
message = ('Embedding Dimension was fixed to be devideable by the number' +
f' of attention heads, is now: {self.embed_dim}')
for func in print, warnings.warn:
func(message)
# Utility Modules
self.autopad = AutoPadToShape((self.new_height, self.width))
self.dropout = nn.Dropout(self.params.dropout)
self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.patch_size, self.width),
keepdim=False)
# Modules with Parameters
self.transformer = TransformerModule(in_shape=self.embed_dim, hidden_size=self.params.lat_dim,
n_heads=self.params.heads, num_layers=self.params.attn_depth,
dropout=self.params.dropout, use_norm=self.params.use_norm,
activation=self.params.activation_as_string
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, self.embed_dim))
self.patch_to_embedding = nn.Linear(patch_dim, self.embed_dim) if self.params.embedding_size \
else F_x(self.embed_dim)
self.cls_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))
self.to_cls_token = nn.Identity()
self.mlp_head = nn.Sequential(
nn.LayerNorm(self.embed_dim),
nn.Linear(self.embed_dim, self.params.lat_dim),
nn.GELU(),
nn.Dropout(self.params.dropout),
nn.Linear(self.params.lat_dim, 1),
nn.Sigmoid()
)
def forward(self, x, mask=None):
"""
:param x: the sequence to the encoder (required).
:param mask: the mask for the src sequence (optional).
:return:
"""
tensor = self.autopad(x)
tensor = self.slider(tensor)
tensor = self.patch_to_embedding(tensor)
b, n, _ = tensor.shape
# cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
cls_tokens = self.cls_token.repeat((b, 1, 1))
tensor = torch.cat((cls_tokens, tensor), dim=1)
tensor += self.pos_embedding[:, :(n + 1)]
tensor = self.dropout(tensor)
tensor = self.transformer(tensor, mask)
tensor = self.to_cls_token(tensor[:, 0])
tensor = self.mlp_head(tensor)
return Namespace(main_out=tensor)

21
models/transformer_model_sequential.py → models/transformer_model_vertical.py
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@@ -7,21 +7,22 @@ from torch import nn
from ml_lib.modules.blocks import TransformerModule
from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, DatasetMixin,
BaseDataloadersMixin, BaseTestMixin)
MIN_NUM_PATCHES = 16
class SequentialVisualTransformer(BinaryMaskDatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
BaseOptimizerMixin,
LightningBaseModule
):
class VerticalVisualTransformer(DatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
BaseTestMixin,
BaseOptimizerMixin,
LightningBaseModule
):
def __init__(self, hparams):
super(SequentialVisualTransformer, self).__init__(hparams)
super(VerticalVisualTransformer, self).__init__(hparams)
# Dataset
# =============================================================================