ResidualModule and New Parameters, Speed Manipulation

models/residual_conv_classifier.py

@@ -0,0 +1,64 @@
+from argparse import Namespace
+
+from torch import nn
+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,
+                                BaseDataloadersMixin)
+
+
+class ResidualConvClassifier(BinaryMaskDatasetFunction,
+                             BaseDataloadersMixin,
+                             BaseTrainMixin,
+                             BaseValMixin,
+                             BaseOptimizerMixin,
+                             LightningBaseModule
+                             ):
+
+    def __init__(self, hparams):
+        super(ResidualConvClassifier, 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
+
+        # Modules with Parameters
+        self.conv_list = ModuleList()
+        last_shape = self.in_shape
+        k = 3  # Base Kernel Value
+        conv_module_params = self.params.module_kwargs
+        conv_module_params.update(conv_kernel=(k, k), conv_stride=(1, 1), conv_padding=1)
+        self.conv_list.append(ConvModule(last_shape, self.conv_filters[0], (k, k), conv_stride=(2, 2), conv_padding=1,
+                                         **self.params.module_kwargs))
+        last_shape = self.conv_list[-1].shape
+        for filters in self.conv_filters:
+            conv_module_params.update(conv_filters=filters)
+            self.conv_list.append(ResidualModule(last_shape, ConvModule, 3, **conv_module_params))
+            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))
+            last_shape = self.conv_list[-1].shape
+
+        self.full_1 = LinearModule(self.conv_list[-1].shape, self.params.lat_dim, **self.params.module_kwargs)
+        self.full_2 = LinearModule(self.full_1.shape, self.full_1.shape * 2, **self.params.module_kwargs)
+        self.full_3 = LinearModule(self.full_2.shape, self.full_2.shape // 2, **self.params.module_kwargs)
+
+        self.full_out = LinearModule(self.full_3.shape, 1, bias=self.params.bias, activation=nn.Sigmoid)
+
+    def forward(self, batch, **kwargs):
+        tensor = batch
+        for conv in self.conv_list:
+            tensor = conv(tensor)
+        tensor = self.full_1(tensor)
+        tensor = self.full_2(tensor)
+        tensor = self.full_3(tensor)
+        tensor = self.full_out(tensor)
+        return Namespace(main_out=tensor)