add input_size to vmf autoencoder

models/rotational_variational_autoencoder.py

@@ -1,5 +1,6 @@
 import os
 import sys
+import math
 
 import torch
 import torch.linalg
@@ -39,11 +40,20 @@ def __init__(self,
         """
         super().__init__()
         self.save_hyperparameters()
-        self.example_input_array = torch.randn(1, 3, 64, 64)
 
-        self.h_dim, self.z_dim, self.distribution = h_dim, z_dim, distribution
+        self.h_dim = h_dim
+        self.z_dim = z_dim
+        self.distribution = distribution
         self.image_size = image_size
-        self.rotations, self.beta, self.spherical_loss_weight = rotations, beta, spherical_loss_weight
+        self.rotations = rotations
+        self.beta = beta
+        self.spherical_loss_weight = spherical_loss_weight
+
+        self.crop_size = int(self.image_size * math.sqrt(2) / 2)
+        self.input_size = 64
+        self.total_input_size = self.input_size * self.input_size * 3
+
+        self.example_input_array = torch.randn(1, 3, self.input_size, self.input_size)
 
         self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=(5,5), stride=2, padding=2)
         self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5,5), stride=2, padding=2)
@@ -71,6 +81,9 @@ def __init__(self,
         self.deconv4 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(4,4), stride=2, padding=1)
         self.deconv5 = nn.ConvTranspose2d(in_channels=16, out_channels=3, kernel_size=(5,5), stride=1, padding=2)
 
+    def get_input_size(self):
+        return self.input_size
+
     def encode(self, x):
         x = F.relu(self.conv1(x))
         x = F.relu(self.conv2(x))