project Refactor, CNN Classifier Basics

lib/modules/blocks.py

@@ -1,11 +1,7 @@
-from abc import ABC
-from pathlib import Path
 from typing import Union
 
 import torch
 from torch import nn
-import torch.nn.functional as F
-import pytorch_lightning as pl
 from lib.modules.utils import AutoPad, Interpolate
 
 #
@@ -26,12 +22,12 @@ class ConvModule(nn.Module):
                  conv_filters=64, conv_kernel=5, conv_stride=1, conv_padding=0):
         super(ConvModule, self).__init__()
 
-        # Module Paramters
+        # Module Parameters
         self.in_shape = in_shape
         in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
         self.activation = activation()
 
-        # Convolution Paramters
+        # Convolution Parameters
         self.padding = conv_padding
         self.stride = conv_stride
 
@@ -44,7 +40,7 @@ class ConvModule(nn.Module):
                                )
 
     def forward(self, x):
-        x = self.norm(x) if self.norm else x
+        x = self.norm(x)
 
         tensor = self.conv(x)
         tensor = self.dropout(tensor)
@@ -72,10 +68,10 @@ class DeConvModule(nn.Module):
         self.in_shape = in_shape
         self.conv_filters = conv_filters
 
-        self.autopad = AutoPad() if autopad else  lambda x: x
-        self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else  lambda x: x
-        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else  lambda x: x
-        self.dropout = nn.Dropout2d(dropout) if dropout else  lambda x: x
+        self.autopad = AutoPad() if autopad else lambda x: x
+        self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
+        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else lambda x: x
+        self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
         self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, conv_kernel, bias=use_bias,
                                           padding=self.padding, stride=self.stride)
 
@@ -100,13 +96,13 @@ class ResidualModule(nn.Module):
         output = self(x)
         return output.shape[1:]
 
-    def __init__(self, in_shape, module_class, n, activation=None, **module_paramters):
+    def __init__(self, in_shape, module_class, n, activation=None, **module_parameters):
         assert n >= 1
         super(ResidualModule, self).__init__()
         self.in_shape = in_shape
-        module_paramters.update(in_shape=in_shape)
+        module_parameters.update(in_shape=in_shape)
         self.activation = activation() if activation else lambda x: x
-        self.residual_block = nn.ModuleList([module_class(**module_paramters) for _ in range(n)])
+        self.residual_block = nn.ModuleList([module_class(**module_parameters) for _ in range(n)])
         assert self.in_shape == self.shape, f'The in_shape: {self.in_shape} - must match the out_shape: {self.shape}.'
 
     def forward(self, x):
@@ -143,5 +139,3 @@ class RecurrentModule(nn.Module):
     def forward(self, x):
         tensor = self.rnn(x)
         return tensor
-
-