Parameter Adjustmens and Ensemble Model Implementation

models/conv_classifier.py

@@ -0,0 +1,75 @@
+from argparse import Namespace
+
+from torch import nn
+from torch.nn import ModuleList
+
+from ml_lib.modules.blocks import ConvModule
+from ml_lib.modules.utils import LightningBaseModule, Flatten
+from util.module_mixins import (BaseOptimizerMixin, BaseTrainMixin, BaseValMixin, BinaryMaskDatasetFunction,
+                                BaseDataloadersMixin)
+
+
+class ConvClassifier(BinaryMaskDatasetFunction,
+                     BaseDataloadersMixin,
+                     BaseTrainMixin,
+                     BaseValMixin,
+                     BaseOptimizerMixin,
+                     LightningBaseModule
+                     ):
+
+    def __init__(self, hparams):
+        super(ConvClassifier, 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()
+
+        # Modules with Parameters
+        self.conv_list = ModuleList()
+        last_shape = self.in_shape
+        k = 3  # Base Kernel Value
+        for filters in self.conv_filters:
+            self.conv_list.append(ConvModule(last_shape, filters, (k, k*2), conv_stride=2, **self.params.module_kwargs))
+            last_shape = self.conv_list[-1].shape
+            self.conv_list.appen(ConvModule(last_shape, filters, 1, conv_stride=1, **self.params.module_kwargs))
+            last_shape = self.conv_list[-1].shape
+            self.conv_list.appen(ConvModule(last_shape, 1, 1, conv_stride=1, **self.params.module_kwargs))
+            last_shape = self.conv_list[-1].shape
+            k = k+2
+
+        self.flat = Flatten(self.conv_list[-1].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.full_3 = nn.Linear(self.full_2.out_features, self.full_2.out_features // 2, self.params.bias)
+
+        self.full_out = nn.Linear(self.full_3.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 = batch
+        for conv in self.conv_list:
+            tensor = conv(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_3(tensor)
+        tensor = self.activation(tensor)
+        tensor = self.dropout(tensor)
+        tensor = self.full_out(tensor)
+        tensor = self.sigmoid(tensor)
+        return Namespace(main_out=tensor)