X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pytorch.git;a=blobdiff_plain;f=lazy_linear.py;h=2fa6a29ef4ab735b1f53fc286e21635e67cf0059;hp=7c9e398a66b16dcc025471b59b6ac874e2a6d5f3;hb=e916a8624b6a09737696c124f35059030f0f20e4;hpb=7443d768bc437889659ba3ed737297f90fe1922e

diff --git a/lazy_linear.py b/lazy_linear.py
index 7c9e398..2fa6a29 100755
--- a/lazy_linear.py
+++ b/lazy_linear.py
@@ -1,8 +1,13 @@
-#!/usr/bin/env python-for-pytorch
+#!/usr/bin/env python
+
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
 
 from torch import nn, Tensor
 
-##########
+######################################################################
 
 class LazyLinear(nn.Module):
 
@@ -23,16 +28,25 @@ class LazyLinear(nn.Module):
 
         return self.core(x)
 
-##########
+    def named_parameters(self, memo=None, prefix=''):
+        assert self.core is not None, 'Parameters not yet defined'
+        return super(LazyLinear, self).named_parameters(memo, prefix)
+
+######################################################################
+
+if __name__ == "__main__":
+    model = nn.Sequential(nn.Conv2d(3, 8, kernel_size = 5),
+                          nn.ReLU(inplace = True),
+                          LazyLinear(128),
+                          nn.ReLU(inplace = True),
+                          nn.Linear(128, 10))
+
+    # model.eval()
 
-model = nn.Sequential(nn.Conv2d(1, 8, kernel_size = 5),
-                      nn.ReLU(inplace = True),
-                      LazyLinear(128),
-                      nn.ReLU(inplace = True),
-                      nn.Linear(128, 10))
+    input = Tensor(100, 3, 32, 32).normal_()
 
-# model.eval()
+    output = model(input)
 
-input = Tensor(100, 1, 32, 32).normal_()
+    for n, x in model.named_parameters():
+        print(n, x.size())
 
-output = model(input)