From a4b6c698c339172725ecc45120e5ee63844592a4 Mon Sep 17 00:00:00 2001
From: Si11ium <steffen.illium@ifi.lmu.de>
Date: Thu, 6 Aug 2020 08:12:07 +0200
Subject: [PATCH] InterSpeech Camera Ready Reporting

---
 modules/blocks.py | 38 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 37 insertions(+), 1 deletion(-)

diff --git a/modules/blocks.py b/modules/blocks.py
index 6b50811..791da14 100644
--- a/modules/blocks.py
+++ b/modules/blocks.py
@@ -5,6 +5,7 @@ import torch
 import warnings
 
 from torch import nn
+from torch.nn import functional as F
 import sys
 sys.path.append(str(Path(__file__).parent))
 from .util import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
@@ -42,14 +43,21 @@ class LinearModule(ShapeMixin, nn.Module):
 class ConvModule(ShapeMixin, nn.Module):
 
     def __init__(self, in_shape, conv_filters, conv_kernel, activation: nn.Module = nn.ELU, pooling_size=None,
-                 bias=True, norm=False, dropout: Union[int, float] = 0,
+                 bias=True, norm=False, dropout: Union[int, float] = 0, trainable: bool = True,
                  conv_class=nn.Conv2d, conv_stride=1, conv_padding=0, **kwargs):
         super(ConvModule, self).__init__()
         assert isinstance(in_shape, (tuple, list)), f'"in_shape" should be a [list, tuple], but was {type(in_shape)}'
         assert len(in_shape) == 3, f'Length should be 3, but was {len(in_shape)}'
         warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
+        if norm and not trainable:
+            warnings.warn('You set this module to be not trainable but the running norm is active.\n' +
+                          'We set it to "eval" mode.\n' +
+                          'Keep this in mind if you do a finetunning or retraining step.'
+                          )
+
         # Module Parameters
         self.in_shape = in_shape
+        self.trainable = trainable
         in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
 
         # Convolution Parameters
@@ -66,6 +74,13 @@ class ConvModule(ShapeMixin, nn.Module):
         self.conv = conv_class(in_channels, self.conv_filters, self.conv_kernel, bias=bias,
                                padding=self.padding, stride=self.stride
                                )
+        if not self.trainable:
+            for param in self.parameters():
+                param.requires_grad = False
+            self.norm = self.norm.eval()
+        else:
+            pass
+
 
     def forward(self, x):
         tensor = self.norm(x)
@@ -76,6 +91,27 @@ class ConvModule(ShapeMixin, nn.Module):
         return tensor
 
 
+# TODO class PreInitializedConvModule(ShapeMixin, nn.Module):
+
+class SobelFilter(ShapeMixin, nn.Module):
+
+    def __init__(self, in_shape):
+        super(SobelFilter, self).__init__()
+
+        self.sobel_x = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).view(1, 1, 3, 3)
+        self.sobel_y = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, 2, -1]]).view(1, 1, 3, 3)
+
+    def forward(self, x):
+        # Apply Filters
+        g_x = F.conv2d(x, self.sobel_x)
+        g_y = F.conv2d(x, self.sobel_y)
+        # Calculate the Edge
+        g = torch.add(*[torch.pow(tensor, 2) for tensor in [g_x, g_y]])
+        # Calculate the Gradient
+        g_grad = torch.atan2(g_x, g_y)
+        return g_x, g_y, g, g_grad
+
+
 class DeConvModule(ShapeMixin, nn.Module):
 
     def __init__(self, in_shape, conv_filters, conv_kernel, conv_stride=1, conv_padding=0,