17 prediction

def detection_preprocess(x,inp_dim,anchors,num_classes,CUDA=False):
        """
        This function will take input_dimension_of_image,anchors and number of classes as input 
        """
        # x --> 4D feature map
        batch_size = x.size(0)
        grid_size = x.size(2)
        stride =  inp_dim // x.size(2)   # factor by which current feature map reduced from input
        #grid_size = inp_dim // stride
    
        bbox_attrs = 5 + num_classes #5 + 80
        num_anchors = len(anchors) #3
    
        #[1, 255, 13, 13]
        prediction = x.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size) # 1x255x169     
        prediction = prediction.transpose(1,2).contiguous() #1x169x255
        prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs) #1x507x85

        # the dimension of anchors is wrt original image.We will make it corresponding to feature map
        anchors = [(a[0]/stride, a[1]/stride) for a in anchors]

        #Sigmoid the  centre_X, centre_Y. and object confidencce
        prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
        prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
        prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
    
        #Add the center offsets
        grid = np.arange(grid_size)
        a,b = np.meshgrid(grid, grid)

        x_offset = torch.FloatTensor(a).view(-1,1) #(1,gridsize*gridsize,1)
        y_offset = torch.FloatTensor(b).view(-1,1)

        if CUDA:
            x_offset = x_offset.cuda()
            y_offset = y_offset.cuda()

        x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
    

        prediction[:,:,:2] += x_y_offset

        #log space transform height and the width
        anchors = torch.FloatTensor(anchors)

        if CUDA:
            anchors = anchors.cuda()

        anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
        prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors #width and height
        prediction[:,:,5: 5 + num_classes] = torch.sigmoid((prediction[:,:, 5 : 5 + num_classes]))    
        prediction[:,:,:4] *= stride    
        return prediction