Bug fix!

README.md

@@ -1,38 +1,41 @@
 # Differentiable Hard Attention Module
 
-This module shifts and scales the input image into a fixed sized image in a smart way. This is a modified version of [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). In their paper, they used another convolutional network to obtain transform parameters. Unlike the original paper, DHAM extract transform parameters with a 1 or 2 convolutional layers without pooling instead of a whole convolutional network. Yet, DHAM uses a weeker transform just to attend on the image, since the objective is to find a region of interest on the input image.
+This module shifts and scales the input image into a fixed sized image in a smart way. This is a modified version of [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025). In their paper, they used another convolutional network to obtain transform parameters. Unlike the original paper, DHAM extract transforms parameters with a 1 or 2 convolutional layers without pooling instead of a whole convolutional network. Yet, DHAM uses a weeker transform just to attend on the image, since the objective is to find a region of interest on the input image.
 
 ![Transformation equation](results/readme_images/eq1.png)
 
 S for scaling and tx, ty are for shifting the attention region. A network with this module can be seen below.
 
 ![Network with dham](results/readme_images/network_with_dham.jpg)
 
-On the above image, there are three convolutional layers before transformation. The third convolutional layer uses spatial softmax as the activation function. This output can be seen as the probability mass function(pmf) in which DHAM performs expected value and expected norm operations to find transform parameters. Scale parameter "s" is obtained by expected L1 or L2 norm and translation parameters are obtained by expected value over indexes of the pmf map.
+On the above image, there are three convolutional layers before transformation. The third convolutional layer uses spatial softmax as the activation function. This output can be seen as the probability mass function(pmf) in which DHAM performs expected value and expected norm operations to find transform parameters. Scale parameter "s" is obtained by expected L1 or L2 norm and translation parameters are obtained by expected value over indexes of the pmf(probability mass function) map.
 
 After these parameters are obtained we can use the transformation mentioned in [STN](https://arxiv.org/abs/1506.02025). They use bilinear transformation to ensure differentiability of the transformation.
 
-Since Dham only uses convolutional layers it can transform the arbitrary-sized input image into a fixed size region. So, there is no limitation of input image's spatial dimensions while training or testing.
+Since Dham only uses convolutional layers it can transform the arbitrary-sized input image into a fixed size region. So, there is no limitation of the input image's spatial dimensions while training or testing.
 
 ## Experiments
 
+- - -
+
 In order to test the module, we used a modified version of the MNIST dataset. We first randomly rescaled the image then randomly sifted on a 120 by 120 map. While training we observed the attention regions as shown below.
 
 ![Network with dham](results/grid_2.png)
 
-Input images and regions of interest for 8 image in a single batch after a few iteration. The input image is 120 by 120 while the transformed images are 28 by 28. There are two convolutional for the module.
 
-Note that, the only objective of the whole network is to classify digits. There is no axillary tasks or losses for attention. The network needs to attend to a proper region in the image in order to classify well.
+Input images and regions of interest for 8 images in a single batch after a few iterations. The input image is 120 by 120 while the transformed images are 28 by 28. There are two convolutional for the module.
+
+> Note that, the only objective of the whole network is to classify digits. There is no axillary tasks or losses for attention. The network needs to attend to a proper region on the image in order to classify well.
 
-The above example was quite easy to attend since in the input image there is nothing but the digit. Therefore, we also performed this experiment with a noisy background.
+The above example was quite an easy one to attend since on the input image there is nothing but the digit. Therefore, we also performed this experiment with a noisy background.
 
 ![Network with dham](results/noise_grid_3.png)
 
-While experimenting with the noisy background, we observed that the number of channel of the convolutional layers for the module needs to be increased when the noise level (SNR) increases. In another word, complicated input images require deeper or wider convolutional layers for attention module.
+While experimenting with the noisy background, we observed that the number of channel of the convolutional layers for the module needs to be increased when the noise level (SNR) increases. In another word, complicated input images require deeper or wider convolutional layers in attention module.
 
 ## Requirements
-
-* Pytorch (0.3.0)
+- - -
+* Pytorch -0.3.0 (Need modifications for >= 4.0)
 * Numpy
 * Matplotlib
 

dham.py

@@ -85,7 +85,7 @@ def bilinear_transorm(self, input, *raw_ind):
         y_raw_ind, x_raw_ind = raw_ind
         B, C, Y, X = input.size()
 
-        y_raw_ind = ((y_raw_ind + 1)*(Y/2)).clamp(0, X-1)
+        y_raw_ind = ((y_raw_ind + 1)*(Y/2)).clamp(0, Y-1)
         x_raw_ind = ((x_raw_ind + 1)*(X/2)).clamp(0, X-1)
 
         x_u = Variable(torch.ceil(x_raw_ind).clamp(0, X-1).data)