Model blocks, Model files, rearrange project structure

lib/models/blocks.py

@@ -156,6 +156,26 @@ class LightningBaseModule(pl.LightningModule, ABC):
         self.apply(_weight_init)
 
 
+class FilterLayer(nn.Module):
+
+    def __init__(self):
+        super(FilterLayer, self).__init__()
+
+    def forward(self, x):
+        tensor = x[:, -1]
+        return tensor
+
+
+class MergingLayer(nn.Module):
+
+    def __init__(self):
+        super(MergingLayer, self).__init__()
+
+    def forward(self, x):
+        # ToDo: Which ones to combine?
+        return
+
+
 #
 # Sub - Modules
 ###################
@@ -241,6 +261,32 @@ class DeConvModule(nn.Module):
         return self.shape
 
 
+class RecurrentModule(nn.Module):
+
+    @property
+    def shape(self):
+        x = torch.randn(self.in_shape).unsqueeze(0)
+        output = self(x)
+        return output.shape[1:]
+
+    def __init__(self, in_shape, hidden_size, num_layers=1, cell_type=nn.GRU, use_bias=True, dropout=0):
+        super(RecurrentModule, self).__init__()
+        self.use_bias = use_bias
+        self.num_layers = num_layers
+        self.in_shape = in_shape
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+        self.rnn = cell_type(self.in_shape[-1] * self.in_shape[-2], hidden_size,
+                             num_layers=num_layers,
+                             bias=self.use_bias,
+                             batch_first=True,
+                             dropout=self.dropout)
+
+    def forward(self, x):
+        tensor = self.rnn(x)
+        return tensor
+
+
 #
 # Full Model Parts
 ###################

lib/models/cnn.py

@@ -0,0 +1,29 @@
+from lib.models.blocks import RecurrentModule, ConvModule, DeConvModule, Generator, LightningBaseModule
+
+
+class CNNRouteGeneratorModel(LightningBaseModule):
+
+    @classmethod
+    def name(cls):
+        pass
+
+    def configure_optimizers(self):
+        pass
+
+    def validation_step(self, *args, **kwargs):
+        pass
+
+    def validation_end(self, outputs):
+        pass
+
+    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+        pass
+
+    def test_step(self, *args, **kwargs):
+        pass
+
+    def __init__(self, *params):
+        super(CNNRouteGeneratorModel, self).__init__(*params)
+
+    def forward(self, x):
+        pass