Improve snoop verbose mode (#18)

tests/test_snoop.py

@@ -23,41 +23,26 @@ def func():
 
 
 verbose_expect = '''
-21:43:42.09 >>> Call to func in File "test_snoop.py", line 16
-21:43:42.09   16 | def func():
-21:43:42.09   17 |     x = torch.tensor(math.inf)
-21:43:42.10 .......... x = tensor(inf)
-21:43:42.10 .......... x.shape = ()
-21:43:42.10 .......... x.dtype = torch.float32
-21:43:42.10 .......... x.device = device(type='cpu')
-21:43:42.10 .......... x.requires_grad = False
-21:43:42.10 .......... x.has_nan = False
-21:43:42.10 .......... x.has_inf = True
-21:43:42.10   18 |     x = torch.tensor(math.nan)
-21:43:42.10 .......... x = tensor(nan)
-21:43:42.10 .......... x.has_nan = True
-21:43:42.10 .......... x.has_inf = False
-21:43:42.10   19 |     x = torch.tensor(1.0, requires_grad=True)
-21:43:42.10 .......... x = tensor(1., requires_grad=True)
-21:43:42.10 .......... x.requires_grad = True
-21:43:42.10 .......... x.has_nan = False
-21:43:42.10   20 |     x = torch.tensor([1.0, math.nan, math.inf])
-21:43:42.10 .......... x = tensor([1., nan, inf])
-21:43:42.10 .......... x.shape = (3,)
-21:43:42.10 .......... x.requires_grad = False
-21:43:42.10 .......... x.has_nan = True
-21:43:42.10 .......... x.has_inf = True
-21:43:42.10   21 |     x = numpy.zeros((2, 2))
-21:43:42.10 .......... x = array([[0., 0.],
-21:43:42.10                       [0., 0.]])
-21:43:42.10 .......... x.shape = (2, 2)
-21:43:42.10 .......... x.dtype = dtype('float64')
-21:43:42.10   22 |     x = (x, x)
-21:43:42.10 .......... x = (array([[0., 0.],
-21:43:42.10                       [0., 0.]]), array([[0., 0.],
-21:43:42.10                       [0., 0.]]))
-21:43:42.10 .......... len(x) = 2
-21:43:42.10 <<< Return value from func: None
+01:24:31.56 >>> Call to func in File "test_snoop.py", line 16
+01:24:31.56   16 | def func():
+01:24:31.56   17 |     x = torch.tensor(math.inf)
+01:24:31.56 .......... x = tensor<(), float32, cpu, has_inf>
+01:24:31.56 .......... x.data = tensor(inf)
+01:24:31.56   18 |     x = torch.tensor(math.nan)
+01:24:31.56 .......... x = tensor<(), float32, cpu, has_nan>
+01:24:31.56 .......... x.data = tensor(nan)
+01:24:31.56   19 |     x = torch.tensor(1.0, requires_grad=True)
+01:24:31.56 .......... x = tensor<(), float32, cpu, grad>
+01:24:31.56 .......... x.data = tensor(1.)
+01:24:31.56   20 |     x = torch.tensor([1.0, math.nan, math.inf])
+01:24:31.56 .......... x = tensor<(3,), float32, cpu, has_nan, has_inf>
+01:24:31.56 .......... x.data = tensor([1., nan, inf])
+01:24:31.56   21 |     x = numpy.zeros((2, 2))
+01:24:31.56 .......... x = ndarray<(2, 2), float64>
+01:24:31.56 .......... x.data = <memory at 0x7efc261e9480>
+01:24:31.56   22 |     x = (x, x)
+01:24:31.56 .......... x = (ndarray<(2, 2), float64>, ndarray<(2, 2), float64>)
+01:24:31.56 <<< Return value from func: None
 '''.strip()
 
 terse_expect = '''

torchsnooper/__init__.py

@@ -155,21 +155,26 @@ def compute_repr(self, x):
 
 def register_snoop(verbose=False, tensor_format=default_format, numpy_format=default_numpy_format):
     import snoop
+    import cheap_repr
+    import snoop.configuration
+    cheap_repr.register_repr(torch.Tensor)(lambda x, _: tensor_format(x))
+    cheap_repr.register_repr(numpy.ndarray)(lambda x, _: numpy_format(x))
+    cheap_repr.cheap_repr(torch.zeros(6))
+    unwanted = {
+        snoop.configuration.len_shape_watch,
+        snoop.configuration.dtype_watch,
+    }
+    snoop.config.watch_extras = tuple(x for x in snoop.config.watch_extras if x not in unwanted)
     if verbose:
+
+        class TensorWrap:
+
+            def __init__(self, tensor):
+                self.tensor = tensor
+
+            def __repr__(self):
+                return self.tensor.__repr__()
+
         snoop.config.watch_extras += (
-            lambda source, value: ('{}.device'.format(source), value.device),
-            lambda source, value: ('{}.requires_grad'.format(source), value.requires_grad),
-            lambda source, value: ('{}.has_nan'.format(source), bool(torch.isnan(value).any())),
-            lambda source, value: ('{}.has_inf'.format(source), bool(torch.isinf(value).any())),
+            lambda source, value: ('{}.data'.format(source), TensorWrap(value.data)),
         )
-    else:
-        import cheap_repr
-        import snoop.configuration
-        cheap_repr.register_repr(torch.Tensor)(lambda x, _: tensor_format(x))
-        cheap_repr.register_repr(numpy.ndarray)(lambda x, _: numpy_format(x))
-        cheap_repr.cheap_repr(torch.zeros(6))
-        unwanted = {
-            snoop.configuration.len_shape_watch,
-            snoop.configuration.dtype_watch,
-        }
-        snoop.config.watch_extras = tuple(x for x in snoop.config.watch_extras if x not in unwanted)