steffen/masks_augments_compare-21
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

requirements

Browse Source
This commit is contained in:
Si11ium
2020-05-14 23:08:36 +02:00
parent 407df15bbf
commit e7d1a4895a
9 changed files with 52 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
models/bandwise_conv_classifier.py
View File

@@ -5,11 +5,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.utils import (LightningBaseModule, HorizontalSplitter, HorizontalMerger)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetFunction,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
class BandwiseConvClassifier(BinaryMaskDatasetFunction,
class BandwiseConvClassifier(BinaryMaskDatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

14
models/bandwise_conv_multihead_classifier.py
View File

@@ -6,11 +6,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.utils import (LightningBaseModule, Flatten, HorizontalSplitter)
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetFunction,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
class BandwiseConvMultiheadClassifier(BinaryMaskDatasetFunction,
class BandwiseConvMultiheadClassifier(BinaryMaskDatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
@@ -42,7 +42,7 @@ class BandwiseConvMultiheadClassifier(BinaryMaskDatasetFunction,
return_dict = {f'band_{band_idx}_val_loss': band_y for band_idx, band_y in enumerate(bands_y_losses)}
last_bce_loss = self.bce_loss(y, batch_y)
return_dict.update(last_bce_loss=last_bce_loss)
return_dict.update(last_val_bce_loss=last_bce_loss)
bands_y_losses.append(last_bce_loss)
combined_loss = torch.stack(bands_y_losses).mean()
@@ -76,7 +76,7 @@ class BandwiseConvMultiheadClassifier(BinaryMaskDatasetFunction,
last_shape = self.split.shape
conv_list = ModuleList()
for filters in self.conv_filters:
conv_list.append(ConvModule(last_shape, filters, (k,k), conv_stride=(1, 1),
conv_list.append(ConvModule(last_shape, filters, (k, k), conv_stride=(2, 2), conv_padding=2,
**self.params.module_kwargs))
last_shape = conv_list[-1].shape
# self.conv_list.append(ConvModule(last_shape, 1, 1, conv_stride=1, **self.params.module_kwargs))
@@ -84,10 +84,10 @@ class BandwiseConvMultiheadClassifier(BinaryMaskDatasetFunction,
self.band_list.append(conv_list)
self.bandwise_deep_list_1 = ModuleList([
LinearModule(self.band_list[0][-1].shape, self.params.lat_dim * 4, **self.params.module_kwargs)
LinearModule(self.band_list[0][-1].shape, self.params.lat_dim, **self.params.module_kwargs)
for _ in range(self.n_band_sections)])
self.bandwise_deep_list_2 = ModuleList([
LinearModule(self.params.lat_dim * 4, self.params.lat_dim * 2, **self.params.module_kwargs)
LinearModule(self.params.lat_dim, self.params.lat_dim * 2, **self.params.module_kwargs)
for _ in range(self.n_band_sections)])
self.bandwise_latent_list = ModuleList([
LinearModule(self.params.lat_dim * 2, self.params.lat_dim, **self.params.module_kwargs)
@@ -96,7 +96,7 @@ class BandwiseConvMultiheadClassifier(BinaryMaskDatasetFunction,
LinearModule(self.params.lat_dim, 1, bias=self.params.bias, activation=nn.Sigmoid)
for _ in range(self.n_band_sections)])
self.full_1 = LinearModule(self.n_band_sections, self.params.lat_dim * 4, **self.params.module_kwargs)
self.full_1 = LinearModule(self.n_band_sections, self.params.lat_dim, **self.params.module_kwargs)
self.full_2 = LinearModule(self.full_1.shape, self.params.lat_dim * 2, **self.params.module_kwargs)
self.full_3 = LinearModule(self.full_2.shape, self.params.lat_dim, **self.params.module_kwargs)
self.full_out = LinearModule(self.full_3.shape, 1, bias=self.params.bias, activation=nn.Sigmoid)

4
models/conv_classifier.py
View File

@@ -5,11 +5,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule
from ml_lib.modules.utils import LightningBaseModule
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetFunction,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
class ConvClassifier(BinaryMaskDatasetFunction,
class ConvClassifier(BinaryMaskDatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,

16
models/ensemble.py
View File

@@ -8,17 +8,17 @@ 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,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
class Ensemble(BinaryMaskDatasetFunction,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
BaseOptimizerMixin,
LightningBaseModule
):
class Ensemble(BinaryMaskDatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
BaseOptimizerMixin,
LightningBaseModule
):
def __init__(self, hparams):
super(Ensemble, self).__init__(hparams)

6
models/residual_conv_classifier.py
View File

@@ -5,11 +5,11 @@ from torch.nn import ModuleList
from ml_lib.modules.blocks import ConvModule, LinearModule, ResidualModule
from ml_lib.modules.utils import LightningBaseModule
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetFunction,
from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetMixin,
BaseDataloadersMixin)
class ResidualConvClassifier(BinaryMaskDatasetFunction,
class ResidualConvClassifier(BinaryMaskDatasetMixin,
BaseDataloadersMixin,
BaseTrainMixin,
BaseValMixin,
@@ -45,6 +45,8 @@ class ResidualConvClassifier(BinaryMaskDatasetFunction,
last_shape = self.conv_list[-1].shape
self.conv_list.append(ConvModule(last_shape, filters, (k, k), conv_stride=(2, 2), conv_padding=2,
**self.params.module_kwargs))
for param in self.conv_list[-1].parameters():
param.requires_grad = False
last_shape = self.conv_list[-1].shape
self.full_1 = LinearModule(self.conv_list[-1].shape, self.params.lat_dim, **self.params.module_kwargs)