Refine the format of printing tensor 2 (#28216)

* refine format

* update doc

* handle uninitialized tensor

* add ut

python/paddle/fluid/tests/unittests/test_var_base.py

@@ -444,6 +444,40 @@ class TestVarBase(unittest.TestCase):
         self.assertEqual(a_str, expected)
         paddle.enable_static()
 
+    def test_tensor_str2(self):
+        paddle.disable_static(paddle.CPUPlace())
+        a = paddle.to_tensor([[1.5111111, 1.0], [0, 0]])
+        a_str = str(a)
+
+        if six.PY2:
+            expected = '''Tensor(shape=[2L, 2L], dtype=float32, place=CPUPlace, stop_gradient=True,
+       [[1.5111, 1.    ],
+        [0.    , 0.    ]])'''
+        else:
+            expected = '''Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=True,
+       [[1.5111, 1.    ],
+        [0.    , 0.    ]])'''
+
+        self.assertEqual(a_str, expected)
+        paddle.enable_static()
+
+    def test_tensor_str3(self):
+        paddle.disable_static(paddle.CPUPlace())
+        a = paddle.to_tensor([[-1.5111111, 1.0], [0, -0.5]])
+        a_str = str(a)
+
+        if six.PY2:
+            expected = '''Tensor(shape=[2L, 2L], dtype=float32, place=CPUPlace, stop_gradient=True,
+       [[-1.5111,  1.    ],
+        [ 0.    , -0.5000]])'''
+        else:
+            expected = '''Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=True,
+       [[-1.5111,  1.    ],
+        [ 0.    , -0.5000]])'''
+
+        self.assertEqual(a_str, expected)
+        paddle.enable_static()
+
 
 class TestVarBaseSetitem(unittest.TestCase):
     def setUp(self):

python/paddle/tensor/to_string.py

@@ -58,18 +58,14 @@ def set_printoptions(precision=None,
             print(a)
             
             '''
-            Tensor: dygraph_tmp_0
-            - place: CPUPlace
-            - shape: [10, 20]
-            - layout: NCHW
-            - dtype: float32
-            - data: [[0.2727, 0.5489, 0.8655, ..., 0.2916, 0.8525, 0.9000],
-                    [0.3806, 0.8996, 0.0928, ..., 0.9535, 0.8378, 0.6409],
-                    [0.1484, 0.4038, 0.8294, ..., 0.0148, 0.6520, 0.4250],
+            Tensor(shape=[10, 20], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
+                   [[0.0002, 0.8503, 0.0135, ..., 0.9508, 0.2621, 0.6661],
+                    [0.9710, 0.2605, 0.9950, ..., 0.4427, 0.9241, 0.9363],
+                    [0.0948, 0.3226, 0.9955, ..., 0.1198, 0.0889, 0.9231],
                     ...,
-                    [0.3426, 0.1909, 0.7240, ..., 0.4218, 0.2676, 0.5679],
-                    [0.5561, 0.2081, 0.0676, ..., 0.9778, 0.3302, 0.9559],
-                    [0.2665, 0.8483, 0.5389, ..., 0.4956, 0.6862, 0.9178]]
+                    [0.7206, 0.0941, 0.5292, ..., 0.4856, 0.1379, 0.0351],
+                    [0.1745, 0.5621, 0.3602, ..., 0.2998, 0.4011, 0.1764],
+                    [0.0728, 0.7786, 0.0314, ..., 0.2583, 0.1654, 0.0637]])
             '''
     """
     kwargs = {}
@@ -101,7 +97,7 @@ def _to_sumary(var):
         return var
     elif len(var.shape) == 1:
         if var.shape[0] > 2 * edgeitems:
-            return paddle.concat([var[:edgeitems], var[-edgeitems:]])
+            return np.concatenate([var[:edgeitems], var[-edgeitems:]])
         else:
             return var
     else:
@@ -109,12 +105,12 @@ def _to_sumary(var):
         if var.shape[0] > 2 * edgeitems:
             begin = [x for x in var[:edgeitems]]
             end = [x for x in var[-edgeitems:]]
-            return paddle.stack([_to_sumary(x) for x in (begin + end)])
+            return np.stack([_to_sumary(x) for x in (begin + end)])
         else:
-            return paddle.stack([_to_sumary(x) for x in var])
+            return np.stack([_to_sumary(x) for x in var])
 
 
-def _format_item(np_var, max_width=0):
+def _format_item(np_var, max_width=0, signed=False):
     if np_var.dtype == np.float32 or np_var.dtype == np.float64 or np_var.dtype == np.float16:
         if DEFAULT_PRINT_OPTIONS.sci_mode:
             item_str = '{{:.{}e}}'.format(
@@ -128,54 +124,66 @@ def _format_item(np_var, max_width=0):
         item_str = '{}'.format(np_var)
 
     if max_width > len(item_str):
-        return '{indent}{data}'.format(
-            indent=(max_width - len(item_str)) * ' ', data=item_str)
-    else:
+        if signed:  # handle sign character for tenosr with negative item
+            if np_var < 0:
+                return item_str.ljust(max_width)
+            else:
+                return ' ' + item_str.ljust(max_width - 1)
+        else:
+            return item_str.ljust(max_width)
+    else:  # used for _get_max_width
         return item_str
 
 
 def _get_max_width(var):
     max_width = 0
-    for item in list(var.numpy().flatten()):
+    signed = False
+    for item in list(var.flatten()):
+        if (not signed) and (item < 0):
+            signed = True
         item_str = _format_item(item)
         max_width = max(max_width, len(item_str))
-    return max_width
 
+    return max_width, signed
 
-def _format_tensor(var, sumary, indent=0):
+
+def _format_tensor(var, sumary, indent=0, max_width=0, signed=False):
     edgeitems = DEFAULT_PRINT_OPTIONS.edgeitems
-    max_width = _get_max_width(_to_sumary(var))
 
     if len(var.shape) == 0:
         # currently, shape = [], i.e., scaler tensor is not supported.
         # If it is supported, it should be formatted like this.
-        return _format_item(var.numpy().item(0), max_width)
+        return _format_item(var.item(0), max_width, signed)
     elif len(var.shape) == 1:
         if sumary and var.shape[0] > 2 * edgeitems:
             items = [
-                _format_item(item, max_width)
-                for item in list(var.numpy())[:DEFAULT_PRINT_OPTIONS.edgeitems]
+                _format_item(item, max_width, signed)
+                for item in list(var)[:DEFAULT_PRINT_OPTIONS.edgeitems]
             ] + ['...'] + [
-                _format_item(item, max_width)
-                for item in list(var.numpy())[-DEFAULT_PRINT_OPTIONS.edgeitems:]
+                _format_item(item, max_width, signed)
+                for item in list(var)[-DEFAULT_PRINT_OPTIONS.edgeitems:]
             ]
         else:
             items = [
-                _format_item(item, max_width) for item in list(var.numpy())
+                _format_item(item, max_width, signed) for item in list(var)
             ]
-
         s = ', '.join(items)
         return '[' + s + ']'
     else:
         # recursively handle all dimensions
         if sumary and var.shape[0] > 2 * edgeitems:
             vars = [
-                _format_tensor(x, sumary, indent + 1) for x in var[:edgeitems]
+                _format_tensor(x, sumary, indent + 1, max_width, signed)
+                for x in var[:edgeitems]
             ] + ['...'] + [
-                _format_tensor(x, sumary, indent + 1) for x in var[-edgeitems:]
+                _format_tensor(x, sumary, indent + 1, max_width, signed)
+                for x in var[-edgeitems:]
             ]
         else:
-            vars = [_format_tensor(x, sumary, indent + 1) for x in var]
+            vars = [
+                _format_tensor(x, sumary, indent + 1, max_width, signed)
+                for x in var
+            ]
 
         return '[' + (',' + '\n' * (len(var.shape) - 1) + ' ' *
                       (indent + 1)).join(vars) + ']'
@@ -190,6 +198,8 @@ def to_string(var, prefix='Tensor'):
     if not tensor._is_initialized():
         return "Tensor(Not initialized)"
 
+    np_var = var.numpy()
+
     if len(var.shape) == 0:
         size = 0
     else:
@@ -201,7 +211,10 @@ def to_string(var, prefix='Tensor'):
     if size > DEFAULT_PRINT_OPTIONS.threshold:
         sumary = True
 
-    data = _format_tensor(var, sumary, indent=indent)
+    max_width, signed = _get_max_width(_to_sumary(np_var))
+
+    data = _format_tensor(
+        np_var, sumary, indent=indent, max_width=max_width, signed=signed)
 
     return _template.format(
         prefix=prefix,