Make the consistency for shape and data type

Summary:
This commit enforces the shape and data type inherited from the same metaclass,
which ensures the consistency between different styles.

docs/api/python/dragon.rst

@@ -15,9 +15,6 @@ dragon
   `class Tensor <dragon/Tensor.html>`_
   `class Tensor <dragon/Tensor.html>`_
   : Tensor abstraction for graph executing.
   : Tensor abstraction for graph executing.
 
 
-  `class TensorSpec <dragon/TensorSpec.html>`_
-  : Spec to describe properties of a tensor.
-
   `class Workspace <dragon/Workspace.html>`_
   `class Workspace <dragon/Workspace.html>`_
   : Sandbox to isolate the resources and computations.
   : Sandbox to isolate the resources and computations.
 
 
@@ -213,7 +210,6 @@ dragon
   dragon/stack
   dragon/stack
   dragon/stop_gradient
   dragon/stop_gradient
   dragon/Tensor
   dragon/Tensor
-  dragon/TensorSpec
   dragon/tile
   dragon/tile
   dragon/transpose
   dragon/transpose
   dragon/where
   dragon/where

docs/api/python/dragon/TensorSpec.rst

-TensorSpec
-==========
-
-.. autoclass:: dragon.TensorSpec
-
-__init__
---------
-.. automethod:: dragon.TensorSpec.__init__
-
-Properties
-----------
-
-dtype
-#####
-.. autoattribute:: dragon.TensorSpec.dtype
-
-name
-####
-.. autoattribute:: dragon.TensorSpec.name
-
-shape
-#####
-.. autoattribute:: dragon.TensorSpec.shape
-
-Methods
--------
-
-is_compatible_with
-##################
-.. automethod:: dragon.TensorSpec.is_compatible_with
-
-.. raw:: html
-
-  <style>
-    h1:before {
-      content: "dragon.";
-      color: #103d3e;
-    }
-  </style>

docs/api/python/tensorflow.rst

@@ -9,6 +9,9 @@ vm.tensorflow
   `class GradientTape <tensorflow/GradientTape.html>`_
   `class GradientTape <tensorflow/GradientTape.html>`_
   : Record the operations for auto differentiation.
   : Record the operations for auto differentiation.
 
 
+  `class TensorShape <tensorflow/TensorShape.html>`_
+  : Represent the a sequence of dimensions.
+
   `class TensorSpec <tensorflow/TensorSpec.html>`_
   `class TensorSpec <tensorflow/TensorSpec.html>`_
   : Spec to describe properties of a tensor.
   : Spec to describe properties of a tensor.
 
 
@@ -124,6 +127,7 @@ vm.tensorflow
   tensorflow/slice
   tensorflow/slice
   tensorflow/split
   tensorflow/split
   tensorflow/squeeze
   tensorflow/squeeze
+  tensorflow/TensorShape
   tensorflow/TensorSpec
   tensorflow/TensorSpec
   tensorflow/transpose
   tensorflow/transpose
   tensorflow/zeros
   tensorflow/zeros

docs/api/python/tensorflow/TensorShape.rst

+TensorShape
+===========
+
+.. autoclass:: dragon.vm.tensorflow.TensorShape
+
+__init__
+--------
+.. automethod:: dragon.vm.tensorflow.TensorShape.__init__
+
+Properties
+----------
+
+dims
+####
+.. autoattribute:: dragon.vm.tensorflow.TensorShape.dims
+
+ndims
+#####
+.. autoattribute:: dragon.vm.tensorflow.TensorShape.ndims
+
+rank
+####
+.. autoattribute:: dragon.vm.tensorflow.TensorShape.rank
+
+Methods
+-------
+
+as_list
+#######
+.. automethod:: dragon.vm.tensorflow.TensorShape.as_list
+
+.. raw:: html
+
+  <style>
+    h1:before {
+      content: "tf.";
+      color: #103d3e;
+    }
+  </style>

docs/api/python/tensorflow/TensorSpec.rst

@@ -26,7 +26,7 @@ shape
 
 
   <style>
   <style>
     h1:before {
     h1:before {
-      content: "dragon.";
+      content: "tf.";
       color: #103d3e;
       color: #103d3e;
     }
     }
   </style>
   </style>

docs/api/python/tensorflow/dtypes/DType.rst

@@ -7,6 +7,76 @@ __init__
 --------
 --------
 .. automethod:: dragon.vm.tensorflow.dtypes.DType.__init__
 .. automethod:: dragon.vm.tensorflow.dtypes.DType.__init__
 
 
+Properties
+----------
+
+as_numpy_dtype
+##############
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.as_numpy_dtype
+
+as_datatype_enum
+################
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.as_datatype_enum
+
+base_dtype
+##########
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.base_dtype
+
+is_numpy_compatible
+###################
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_numpy_compatible
+
+is_bool
+#######
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_bool
+
+is_complex
+##########
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_complex
+
+is_floating
+###########
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_floating
+
+is_integer
+##########
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_integer
+
+is_quantized
+############
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_quantized
+
+is_unsigned
+###########
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.is_unsigned
+
+limits
+######
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.limits
+
+max
+###
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.max
+
+min
+###
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.min
+
+name
+####
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.name
+
+real_dtype
+##########
+.. autoattribute:: dragon.vm.tensorflow.dtypes.DType.real_dtype
+
+Methods
+-------
+
+is_compatible_with
+##################
+.. automethod:: dragon.vm.tensorflow.dtypes.DType.is_compatible_with
+
 .. raw:: html
 .. raw:: html
 
 
   <style>
   <style>

docs/api/python/torch.rst

@@ -7,7 +7,10 @@ vm.torch
   #######
   #######
 
 
   `class device <torch/device.html>`_
   `class device <torch/device.html>`_
-  : Represent the device where tensor will be allocated.
+  : Represent the device spec.
+
+  `class dtype <torch/device.html>`_
+  : The basic data type.
 
 
   `class enable_grad <torch/enable_grad.html>`_
   `class enable_grad <torch/enable_grad.html>`_
   : Context-manager to enable gradient calculation.
   : Context-manager to enable gradient calculation.
@@ -265,6 +268,7 @@ vm.torch
   torch/cumsum
   torch/cumsum
   torch/device
   torch/device
   torch/div
   torch/div
+  torch/dtype
   torch/empty
   torch/empty
   torch/enable_grad
   torch/enable_grad
   torch/eq
   torch/eq

docs/api/python/torch/dtype.rst

+dtype
+=====
+
+.. autoclass:: dragon.vm.torch.dtype
+
+__init__
+--------
+.. automethod:: dragon.vm.torch.dtype.__init__
+
+.. raw:: html
+
+  <style>
+    h1:before {
+      content: "torch.";
+      color: #103d3e;
+    }
+  </style>

dragon/operators/array/arange_op.cc

@@ -21,7 +21,7 @@ void ArangeOp<Context>::DoRunWithType() {
 
 
   // Determine the generating range
   // Determine the generating range
   // Values are in a half-open interval: [start, stop)
   // Values are in a half-open interval: [start, stop)
-  auto count = (int64_t)std::round((stop - start) / step);
+  auto count = (int64_t)std::ceil((stop - start) / step);
   CHECK_GT(count, 0) << "\nInvalid generating range: "
   CHECK_GT(count, 0) << "\nInvalid generating range: "
                      << "[" << start << ", " << stop << ") with step = " << step
                      << "[" << start << ", " << stop << ") with step = " << step
                      << ".";
                      << ".";

dragon/python/__init__.py

@@ -39,7 +39,6 @@ from dragon import vm
 from dragon.core.autograph.tensor import Tensor
 from dragon.core.autograph.tensor import Tensor
 from dragon.core.eager.tensor import EagerTensor
 from dragon.core.eager.tensor import EagerTensor
 from dragon.core.eager.backprop import GradientTape
 from dragon.core.eager.backprop import GradientTape
-from dragon.core.framework.tensor_spec import TensorSpec
 from dragon.core.framework.workspace import Workspace
 from dragon.core.framework.workspace import Workspace
 
 
 # Function
 # Function

dragon/python/core/autograph/def_function.py

@@ -254,7 +254,7 @@ class FunctionGuard(object):
                             )
                             )
                         shape = input_signature[i].shape
                         shape = input_signature[i].shape
                         dtype = input_signature[i].dtype
                         dtype = input_signature[i].dtype
-                    inputs.append(Tensor(name, shape, dtype).constant())
+                    inputs.append(Tensor(shape, dtype, name).constant())
             with context.name_scope('${%d}' % id(self)), eager_context.graph_mode():
             with context.name_scope('${%d}' % id(self)), eager_context.graph_mode():
                 returns = nest.flatten(self._python_function(*inputs))
                 returns = nest.flatten(self._python_function(*inputs))
             outputs, dummies = [], []
             outputs, dummies = [], []
@@ -328,8 +328,8 @@ def function(func=None, input_signature=None):
 
 
     ```python
     ```python
     @dragon.function(input_signature=[
     @dragon.function(input_signature=[
-        dragon.TensorSpec(shape=[], dtype='float32'),
-        dragon.TensorSpec(shape=[], dtype='float32')
+        dragon.Tensor(shape=[], dtype='float32'),
+        dragon.Tensor(shape=[], dtype='float32'),
     ])
     ])
     def foo(x, y):
     def foo(x, y):
         return dragon.math.add([x + y, x])
         return dragon.math.add([x + y, x])
@@ -341,8 +341,8 @@ def function(func=None, input_signature=None):
     ----------
     ----------
     func : callable, optional
     func : callable, optional
         The function to be compiled.
         The function to be compiled.
-    input_signature : Sequence[dragon.TensorSpec], optional
-        The specs to hint the input info.
+    input_signature : Sequence[dragon.Tensor], optional
+        The tensors to hint the input info.
 
 
     Returns
     Returns
     -------
     -------

dragon/python/core/autograph/function_lib.py

@@ -20,9 +20,10 @@ import os
 from dragon.core.autograph import grad_maker
 from dragon.core.autograph import grad_maker
 from dragon.core.autograph.op_def import OpDef
 from dragon.core.autograph.op_def import OpDef
 from dragon.core.autograph.op_def import OpInfo
 from dragon.core.autograph.op_def import OpInfo
-from dragon.core.autograph.tensor import Tensor
+from dragon.core.autograph.tensor import TensorRef
 from dragon.core.framework import config
 from dragon.core.framework import config
 from dragon.core.framework import proto_util
 from dragon.core.framework import proto_util
+from dragon.core.framework import types
 from dragon.core.framework import workspace
 from dragon.core.framework import workspace
 from dragon.core.proto import dragon_pb2
 from dragon.core.proto import dragon_pb2
 from dragon.core.util import logging
 from dragon.core.util import logging
@@ -107,9 +108,7 @@ def add_update_defs(graph_def, optimizer):
                 name=OpDef.get_name(),
                 name=OpDef.get_name(),
                 operation='MEAN',
                 operation='MEAN',
                 communication='ALLREDUCE',
                 communication='ALLREDUCE',
-                **process_group.arguments
-            )
-        )
+                **process_group.arguments))
     graph_def.op.extend(update_defs)
     graph_def.op.extend(update_defs)
 
 
 
 
@@ -147,14 +146,11 @@ class Function(object):
         if givens is not None:
         if givens is not None:
             name_dict = {}
             name_dict = {}
             for k, v in givens.items():
             for k, v in givens.items():
-                if isinstance(v, Tensor):
+                if types.is_symbolic_tensor(v):
                     name_dict[k.id] = v.id
                     name_dict[k.id] = v.id
                     op_info.merge_from(v)
                     op_info.merge_from(v)
                 else:
                 else:
-                    raise ValueError(
-                        'Excepted a Tensor, '
-                        'got {}.'.format(type(v).__name__)
-                    )
+                    raise ValueError('Excepted a Tensor, got {}.'.format(type(v).__name__))
             # Update the original defs.
             # Update the original defs.
             op_info = copy.deepcopy(op_info)
             op_info = copy.deepcopy(op_info)
             for k in op_info._defs.keys():
             for k in op_info._defs.keys():
@@ -257,8 +253,8 @@ class Function(object):
             The self.
             The self.
 
 
         """
         """
-        self.outputs = [Tensor(name) for name in graph_def.output]
-        self.inputs = [Tensor(name).constant() for name in graph_def.input]
+        self.outputs = [TensorRef(name) for name in graph_def.output]
+        self.inputs = [TensorRef(name).constant() for name in graph_def.input]
 
 
         # Fill with all known graph elements.
         # Fill with all known graph elements.
         add_device_option(graph_def)
         add_device_option(graph_def)
@@ -293,7 +289,7 @@ def create_function(inputs=None, outputs=None, givens=None, optimizer=None):
     Tensors that catch any operators can be used to create a graph:
     Tensors that catch any operators can be used to create a graph:
 
 
     ```python
     ```python
-    x = dragon.Tensor('x', dtype='float32').constant()
+    x = dragon.Tensor(dtype='float32').constant()
     y = x * 2
     y = x * 2
     f = dragon.create_function(outputs=y)
     f = dragon.create_function(outputs=y)
     ```
     ```
@@ -315,20 +311,20 @@ def create_function(inputs=None, outputs=None, givens=None, optimizer=None):
     Specify ``givens`` to substitute tensors before creating:
     Specify ``givens`` to substitute tensors before creating:
 
 
     ```python
     ```python
-    x = dragon.Tensor('x', dtype='float32').constant()
+    x = dragon.Tensor(dtype='float32').constant()
     y = x * 2
     y = x * 2
     foo = dragon.create_function(outputs=y)
     foo = dragon.create_function(outputs=y)
 
 
     # "bar" takes "x2" as input, and also writes to "y"
     # "bar" takes "x2" as input, and also writes to "y"
-    x2 = dragon.Tensor('x2', dtype='float32').constant()
+    x2 = dragon.Tensor(dtype='float32').constant()
     bar = dragon.create_function(outputs=y, givens={x: x2})
     bar = dragon.create_function(outputs=y, givens={x: x2})
     ```
     ```
 
 
     Specify ``optimizer`` to make a graph applying parameter updates:
     Specify ``optimizer`` to make a graph applying parameter updates:
 
 
     ```python
     ```python
-    x = dragon.Tensor('x', dtype='float32').set_value(1)
-    x_grad = dragon.Tensor('x_grad', dtype='float32').set_value(1)
+    x = dragon.Tensor(dtype='float32').set_value(1)
+    x_grad = dragon.Tensor(dtype='float32').set_value(1)
 
 
     optimizer = dragon.optimizers.SGD(base_lr=0.01)
     optimizer = dragon.optimizers.SGD(base_lr=0.01)
     optimizer.apply_gradients(values_and_grads=[(x, x_grad)])
     optimizer.apply_gradients(values_and_grads=[(x, x_grad)])

dragon/python/core/autograph/op_spec.py

@@ -41,7 +41,7 @@ def arange_spec(args, inputs, outputs):
     else:
     else:
         start, stop, step = slice_args
         start, stop, step = slice_args
     try:
     try:
-        outputs[0].shape = [int(round((stop - start) / step))]
+        outputs[0].shape = (int(math.ceil((stop - start) / step)),)
     except TypeError:
     except TypeError:
         pass
         pass
     return outputs
     return outputs
@@ -53,33 +53,40 @@ def arg_reduce_spec(args, inputs, outputs):
     axis, top_k = args['axis'], args['top_k']
     axis, top_k = args['axis'], args['top_k']
     if args['keep_dims']:
     if args['keep_dims']:
         if axis is None:
         if axis is None:
-            outputs[0].shape = [1]
+            outputs[0].shape = (1,)
         else:
         else:
             try:
             try:
-                outputs[0].shape = inputs[0].shape[:]
-                if axis < len(outputs[0].shape):
-                    outputs[0].shape[axis] = top_k
-            except TypeError:
+                out_shape = list(inputs[0].shape[:])
+                out_shape[axis] = top_k
+                outputs[0].shape = out_shape
+            except (TypeError, IndexError):
                 pass
                 pass
     else:
     else:
         if axis is None:
         if axis is None:
             if top_k > 1:
             if top_k > 1:
-                outputs[0].shape = [top_k]
+                outputs[0].shape = (top_k,)
             else:
             else:
-                outputs[0].shape = []
+                outputs[0].shape = ()
         else:
         else:
             try:
             try:
-                outputs[0].shape = inputs[0].shape[:]
-                if axis < len(outputs[0].shape):
+                out_shape = list(inputs[0].shape[:])
+                if axis < len(out_shape):
                     if top_k > 1:
                     if top_k > 1:
-                        outputs[0].shape[axis] = top_k
+                        out_shape[axis] = top_k
                     else:
                     else:
-                        del outputs[0].shape[axis]
-            except TypeError:
+                        del out_shape[axis]
+                outputs[0].shape = out_shape
+            except (TypeError, IndexError):
                 pass
                 pass
     return outputs
     return outputs
 
 
 
 
+@register(['Assign', 'MaskedAssign'])
+def assign_spec(args, inputs, outputs):
+    _ = locals()
+    return outputs
+
+
 def binary_shape_spec(inputs, outputs):
 def binary_shape_spec(inputs, outputs):
     if inputs[0].shape is None or inputs[1].shape is None:
     if inputs[0].shape is None or inputs[1].shape is None:
         return outputs
         return outputs
@@ -158,7 +165,7 @@ def concat_spec(args, inputs, outputs):
     out_shape = None
     out_shape = None
     for input in inputs:
     for input in inputs:
         if out_shape is None and input.shape is not None:
         if out_shape is None and input.shape is not None:
-            out_shape = input.shape[:]
+            out_shape = list(input.shape[:])
     try:
     try:
         for i in range(len(out_shape)):
         for i in range(len(out_shape)):
             for input in inputs:
             for input in inputs:
@@ -188,7 +195,7 @@ def conv_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     out_shape = None
     out_shape = None
     try:
     try:
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         num_axes = len(out_shape) - 2
         num_axes = len(out_shape) - 2
         channel_axis = 1 if args['data_format'] == 'NCHW' else -1
         channel_axis = 1 if args['data_format'] == 'NCHW' else -1
         spatial_axis = 2 if args['data_format'] == 'NCHW' else 1
         spatial_axis = 2 if args['data_format'] == 'NCHW' else 1
@@ -212,7 +219,7 @@ def conv_spec(args, inputs, outputs):
                 out_size = None
                 out_size = None
             out_shape[i + spatial_axis] = out_size
             out_shape[i + spatial_axis] = out_size
     except (TypeError, IndexError):
     except (TypeError, IndexError):
-        pass
+        out_shape = None
     outputs[0].shape = out_shape
     outputs[0].shape = out_shape
     return outputs
     return outputs
 
 
@@ -222,7 +229,7 @@ def conv_transpose_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     out_shape = None
     out_shape = None
     try:
     try:
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         num_axes = len(out_shape) - 2
         num_axes = len(out_shape) - 2
         channel_axis = 1 if args['data_format'] == 'NCHW' else -1
         channel_axis = 1 if args['data_format'] == 'NCHW' else -1
         spatial_axis = 2 if args['data_format'] == 'NCHW' else 1
         spatial_axis = 2 if args['data_format'] == 'NCHW' else 1
@@ -251,7 +258,7 @@ def conv_transpose_spec(args, inputs, outputs):
                 out_size = None
                 out_size = None
             out_shape[i + spatial_axis] = out_size
             out_shape[i + spatial_axis] = out_size
     except (TypeError, IndexError):
     except (TypeError, IndexError):
-        pass
+        out_shape = None
     outputs[0].shape = out_shape
     outputs[0].shape = out_shape
     return outputs
     return outputs
 
 
@@ -261,7 +268,7 @@ def depth_to_space_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     try:
     try:
         bs = args['block_size']
         bs = args['block_size']
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         num_axes = len(out_shape) - 2
         num_axes = len(out_shape) - 2
         if len(out_shape) < 3:
         if len(out_shape) < 3:
             return outputs
             return outputs
@@ -326,7 +333,7 @@ def expand_spec(args, inputs, outputs):
     if shape is None:
     if shape is None:
         return outputs
         return outputs
     try:
     try:
-        in_shape, out_shape = inputs[0].shape[:], shape[:]
+        in_shape, out_shape = list(inputs[0].shape[:]), list(shape[:])
         if len(shape) < len(in_shape):
         if len(shape) < len(in_shape):
             num_keep = len(in_shape) - len(shape)
             num_keep = len(in_shape) - len(shape)
             out_shape = in_shape[:num_keep] + out_shape
             out_shape = in_shape[:num_keep] + out_shape
@@ -347,7 +354,7 @@ def expand_dims_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     axes = [] if args['axes'] is None else args['axes']
     axes = [] if args['axes'] is None else args['axes']
     try:
     try:
-        out_shape = inputs[0].shape[:] + [0] * len(axes)
+        out_shape = list(inputs[0].shape[:]) + [0] * len(axes)
         out_rank = len(out_shape)
         out_rank = len(out_shape)
         for axis in axes:
         for axis in axes:
             while axis < 0:
             while axis < 0:
@@ -364,7 +371,7 @@ def expand_dims_spec(args, inputs, outputs):
                     break
                     break
                 out_shape[i] = inputs[0].shape[j]
                 out_shape[i] = inputs[0].shape[j]
                 j += 1
                 j += 1
-        outputs[0].shape = list(filter(lambda x: x != 0, out_shape))
+        outputs[0].shape = tuple(filter(lambda x: x != 0, out_shape))
     except TypeError:
     except TypeError:
         pass
         pass
     return outputs
     return outputs
@@ -402,7 +409,7 @@ def flatten_spec(args, inputs, outputs):
     else:
     else:
         out_shape = None
         out_shape = None
     try:
     try:
-        in_shape = inputs[0].shape[:]
+        in_shape = list(inputs[0].shape[:])
         if keep_axes is not None:
         if keep_axes is not None:
             if len(in_shape) <= keep_axes:
             if len(in_shape) <= keep_axes:
                 out_shape[:len(in_shape)] = in_shape
                 out_shape[:len(in_shape)] = in_shape
@@ -422,10 +429,7 @@ def flatten_spec(args, inputs, outputs):
                 num_flatten = math_util.prod(in_shape[axis:axis + num_axes])
                 num_flatten = math_util.prod(in_shape[axis:axis + num_axes])
             except TypeError:
             except TypeError:
                 num_flatten = None
                 num_flatten = None
-            out_shape = \
-                in_shape[: axis] + \
-                [num_flatten] + \
-                in_shape[axis + num_axes:]
+            out_shape = in_shape[: axis] + [num_flatten] + in_shape[axis + num_axes:]
     except (TypeError, IndexError):
     except (TypeError, IndexError):
         pass
         pass
     outputs[0].shape = out_shape
     outputs[0].shape = out_shape
@@ -441,7 +445,7 @@ def fully_connected_spec(args, inputs, outputs):
             axis += len(inputs[0].shape)
             axis += len(inputs[0].shape)
         except TypeError:
         except TypeError:
             return outputs
             return outputs
-    outputs[0].shape = [None] * (axis + 1)
+    out_shape = [None] * (axis + 1)
     if out_channels is None:
     if out_channels is None:
         try:
         try:
             if args['transW']:
             if args['transW']:
@@ -450,11 +454,12 @@ def fully_connected_spec(args, inputs, outputs):
                 out_channels = inputs[1].shape[1]
                 out_channels = inputs[1].shape[1]
         except (TypeError, IndexError):
         except (TypeError, IndexError):
             out_channels = None
             out_channels = None
-    outputs[0].shape[axis] = out_channels
     try:
     try:
-        outputs[0].shape[:axis] = inputs[0].shape[:axis]
-    except TypeError:
+        out_shape[axis] = out_channels
+        out_shape[:axis] = inputs[0].shape[:axis]
+    except (TypeError, IndexError):
         pass
         pass
+    outputs[0].shape = out_shape
     return outputs
     return outputs
 
 
 
 
@@ -467,11 +472,12 @@ def channel_normalize_spec(args, inputs, outputs):
     try:
     try:
         if perm is None:
         if perm is None:
             perm = list(range((len(inputs[0].shape))))
             perm = list(range((len(inputs[0].shape))))
-        outputs[0].shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         for i, axis in enumerate(perm):
         for i, axis in enumerate(perm):
-            outputs[0].shape[i] = inputs[0].shape[axis]
+            out_shape[i] = inputs[0].shape[axis]
     except (TypeError, IndexError):
     except (TypeError, IndexError):
-        outputs[0].shape = None
+        out_shape = None
+    outputs[0].shape = out_shape
     return outputs
     return outputs
 
 
 
 
@@ -512,7 +518,7 @@ def is_spec(args, inputs, outputs):
 def masked_select_spec(args, inputs, outputs):
 def masked_select_spec(args, inputs, outputs):
     _ = locals()
     _ = locals()
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
-    outputs[0].shape = [None]
+    outputs[0].shape = (None,)
     return outputs
     return outputs
 
 
 
 
@@ -522,8 +528,8 @@ def matmul_spec(args, inputs, outputs):
     ta, tb = args['transA'], args['transB']
     ta, tb = args['transA'], args['transB']
     out_shape = None
     out_shape = None
     try:
     try:
-        b_shape = inputs[1].shape[:]
-        a_shape = out_shape = inputs[0].shape[:]
+        b_shape = list(inputs[1].shape[:])
+        a_shape = out_shape = list(inputs[0].shape[:])
         out_shape[-2] = a_shape[-1] if ta else a_shape[-2]
         out_shape[-2] = a_shape[-1] if ta else a_shape[-2]
         out_shape[-1] = b_shape[-2] if tb else b_shape[-1]
         out_shape[-1] = b_shape[-2] if tb else b_shape[-1]
     except TypeError:
     except TypeError:
@@ -538,7 +544,7 @@ def moments_spec(args, inputs, outputs):
         inputs[0].dtype if inputs[0].dtype == 'float64' else 'float32'
         inputs[0].dtype if inputs[0].dtype == 'float64' else 'float32'
     axes, keep_dims = args['axes'], args['keep_dims']
     axes, keep_dims = args['axes'], args['keep_dims']
     try:
     try:
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         for axis in axes:
         for axis in axes:
             if axis < len(out_shape):
             if axis < len(out_shape):
                 out_shape[axis] = 1
                 out_shape[axis] = 1
@@ -550,7 +556,7 @@ def moments_spec(args, inputs, outputs):
             out_shape = squeezed_shape
             out_shape = squeezed_shape
     except TypeError:
     except TypeError:
         out_shape = None
         out_shape = None
-    outputs[0].shape = outputs[1].shape = out_shape if axes else []
+    outputs[0].shape = outputs[1].shape = out_shape if axes else ()
     return outputs
     return outputs
 
 
 
 
@@ -570,7 +576,7 @@ def non_zero_spec(args, inputs, outputs):
     _ = locals()
     _ = locals()
     outputs[0].dtype = 'int64'
     outputs[0].dtype = 'int64'
     try:
     try:
-        outputs[0].shape = [None, len(inputs[0].shape)]
+        outputs[0].shape = (None, len(inputs[0].shape))
     except TypeError:
     except TypeError:
         pass
         pass
     return outputs
     return outputs
@@ -580,8 +586,7 @@ def non_zero_spec(args, inputs, outputs):
 def one_hot_spec(args, inputs, outputs):
 def one_hot_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     try:
     try:
-        outputs[0].shape = inputs[0].shape[:]
-        outputs[0].shape.append(args['depth'])
+        outputs[0].shape = inputs[0].shape[:] + (args['depth'],)
     except TypeError:
     except TypeError:
         pass
         pass
     return outputs
     return outputs
@@ -593,14 +598,14 @@ def pad_spec(args, inputs, outputs):
     pads, num_dims = args['pads'], len(args['pads']) // 2
     pads, num_dims = args['pads'], len(args['pads']) // 2
     out_shape = None
     out_shape = None
     try:
     try:
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         for i in range(num_dims):
         for i in range(num_dims):
             if i < len(out_shape):
             if i < len(out_shape):
                 try:
                 try:
                     out_shape[i] += (pads[i] + pads[i + num_dims])
                     out_shape[i] += (pads[i] + pads[i + num_dims])
                 except TypeError:
                 except TypeError:
                     out_shape[i] = None
                     out_shape[i] = None
-    except TypeError:
+    except (TypeError, IndexError):
         pass
         pass
     outputs[0].shape = out_shape
     outputs[0].shape = out_shape
     return outputs
     return outputs
@@ -654,22 +659,22 @@ def reduce_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     axes, keep_dims = args['axes'], args['keep_dims']
     axes, keep_dims = args['axes'], args['keep_dims']
     if axes is None:
     if axes is None:
-        output_shape = []
+        outputs[0].shape = ()
     else:
     else:
         try:
         try:
-            output_shape = inputs[0].shape[:]
+            out_shape = list(inputs[0].shape[:])
             for axis in axes:
             for axis in axes:
-                if axis < len(output_shape):
-                    output_shape[axis] = 1
+                if axis < len(out_shape):
+                    out_shape[axis] = 1
             if not keep_dims:
             if not keep_dims:
                 squeezed_shape = []
                 squeezed_shape = []
-                for d in output_shape:
+                for d in out_shape:
                     if d != 1:
                     if d != 1:
                         squeezed_shape.append(d)
                         squeezed_shape.append(d)
-                output_shape = squeezed_shape
-        except TypeError:
-            output_shape = None
-    outputs[0].shape = output_shape
+                out_shape = squeezed_shape
+            outputs[0].shape = out_shape
+        except (TypeError, IndexError):
+            pass
     return outputs
     return outputs
 
 
 
 
@@ -680,19 +685,20 @@ def repeat_spec(args, inputs, outputs):
     if axis is None:
     if axis is None:
         try:
         try:
             num_elements = math_util.prod(inputs[0].shape[:])
             num_elements = math_util.prod(inputs[0].shape[:])
-            outputs[0].shape = [num_elements * repeats]
+            outputs[0].shape = (num_elements * repeats,)
         except TypeError:
         except TypeError:
-            outputs[0].shape = [None]
+            outputs[0].shape = (None,)
     else:
     else:
         try:
         try:
-            outputs[0].shape = inputs[0].shape[:]
+            out_shape = list(inputs[0].shape[:])
         except TypeError:
         except TypeError:
             return outputs
             return outputs
-        if axis < len(outputs[0].shape):
+        if axis < len(out_shape):
             try:
             try:
-                outputs[0].shape[axis] *= repeats
+                out_shape[axis] *= repeats
             except TypeError:
             except TypeError:
-                outputs[0].shape[axis] = None
+                out_shape[axis] = None
+        outputs[0].shape = out_shape
     return outputs
     return outputs
 
 
 
 
@@ -739,29 +745,29 @@ def resize_spec(args, inputs, outputs):
        'scales_descs' in args:
        'scales_descs' in args:
         return outputs
         return outputs
     try:
     try:
-        out_dims = inputs[0].shape[:]
-        num_axes = len(out_dims) - 2
-        axis = len(out_dims) - 2 if args['data_format'] == 'NCHW' else 1
+        out_shape = list(inputs[0].shape[:])
+        num_axes = len(out_shape) - 2
+        axis = len(out_shape) - 2 if args['data_format'] == 'NCHW' else 1
         try:
         try:
             for i in range(num_axes):
             for i in range(num_axes):
                 j = axis + i
                 j = axis + i
                 if args['sizes'] is not None:
                 if args['sizes'] is not None:
                     if len(args['sizes']) == 1:
                     if len(args['sizes']) == 1:
-                        out_dims[j] = args['sizes'][0]
+                        out_shape[j] = args['sizes'][0]
                     elif len(args['sizes']) == num_axes:
                     elif len(args['sizes']) == num_axes:
-                        out_dims[j] = args['sizes'][i]
+                        out_shape[j] = args['sizes'][i]
                     else:
                     else:
-                        out_dims[j] = args['sizes'][j]
+                        out_shape[j] = args['sizes'][j]
                 elif args['scales'] is not None:
                 elif args['scales'] is not None:
                     if len(args['scales']) == 1:
                     if len(args['scales']) == 1:
-                        out_dims[j] = int(out_dims[j] * args['scales'][0])
+                        out_shape[j] = int(out_shape[j] * args['scales'][0])
                     elif len(args['scales']) == num_axes:
                     elif len(args['scales']) == num_axes:
-                        out_dims[j] = int(out_dims[j] * args['scales'][i])
+                        out_shape[j] = int(out_shape[j] * args['scales'][i])
                     else:
                     else:
-                        out_dims[j] = int(out_dims[j] * args['sizes'][j])
+                        out_shape[j] = int(out_shape[j] * args['sizes'][j])
         except IndexError:
         except IndexError:
             return outputs
             return outputs
-        outputs[0].shape = out_dims
+        outputs[0].shape = out_shape
     except TypeError:
     except TypeError:
         pass
         pass
     return outputs
     return outputs
@@ -773,7 +779,7 @@ def roi_pool_spec(args, inputs, outputs):
     pool_h, pool_w = args['pooled_h'], args['pooled_w']
     pool_h, pool_w = args['pooled_h'], args['pooled_w']
     out_shape = None
     out_shape = None
     try:
     try:
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         out_shape[2:4] = pool_h, pool_w
         out_shape[2:4] = pool_h, pool_w
         try:
         try:
             out_shape[0] = inputs[1].shape[0]
             out_shape[0] = inputs[1].shape[0]
@@ -833,11 +839,12 @@ def softmax_loss_spec(args, inputs, outputs):
     outputs[0].dtype = 'float32'
     outputs[0].dtype = 'float32'
     axis, reduction = args['axis'], args['reduction']
     axis, reduction = args['axis'], args['reduction']
     if reduction != 'NONE':
     if reduction != 'NONE':
-        outputs[0].shape = []
+        outputs[0].shape = ()
     else:
     else:
         try:
         try:
-            outputs[0].shape = inputs[0].shape[:]
-            outputs[0].shape.pop(axis)
+            out_shape = list(inputs[0].shape[:])
+            out_shape.pop(axis)
+            outputs[0].shape = out_shape
         except (TypeError, IndexError):
         except (TypeError, IndexError):
             pass
             pass
     return outputs
     return outputs
@@ -848,7 +855,7 @@ def space_to_depth_spec(args, inputs, outputs):
     outputs[0].dtype = inputs[0].dtype
     outputs[0].dtype = inputs[0].dtype
     try:
     try:
         bs = args['block_size']
         bs = args['block_size']
-        out_shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         num_axes = len(out_shape) - 2
         num_axes = len(out_shape) - 2
         if len(out_shape) < 3:
         if len(out_shape) < 3:
             return outputs
             return outputs
@@ -885,14 +892,14 @@ def split_spec(args, inputs, outputs):
         try:
         try:
             if axis >= len(inputs[0].shape[:]):
             if axis >= len(inputs[0].shape[:]):
                 return outputs
                 return outputs
-            outputs[i].shape = inputs[0].shape[:]
+            out_shape = list(inputs[0].shape[:])
         except TypeError:
         except TypeError:
             return outputs
             return outputs
         if size_splits is not None:
         if size_splits is not None:
             try:
             try:
-                outputs[i].shape[axis] = size_splits[i]
+                out_shape[axis] = size_splits[i]
             except IndexError:
             except IndexError:
-                outputs[i].shape[axis] = None
+                return outputs
         elif slice_points is not None:
         elif slice_points is not None:
             try:
             try:
                 if i < len(outputs) - 1:
                 if i < len(outputs) - 1:
@@ -900,14 +907,18 @@ def split_spec(args, inputs, outputs):
                     slice_offset += slice_dim
                     slice_offset += slice_dim
                 else:
                 else:
                     slice_dim = inputs[0].shape[axis] - slice_offset
                     slice_dim = inputs[0].shape[axis] - slice_offset
+                out_shape[axis] = slice_dim
             except (TypeError, IndexError):
             except (TypeError, IndexError):
-                slice_dim = None
-            outputs[i].shape[axis] = slice_dim
+                return outputs
         else:
         else:
             try:
             try:
-                outputs[i].shape[axis] //= num_outputs
-            except TypeError:
-                outputs[i].shape[axis] = None
+                slice_dim = (out_shape[axis] + num_outputs - 1) // num_outputs
+                if i == num_outputs - 1:
+                    slice_dim = out_shape[axis] - slice_dim * (num_outputs - 1)
+                out_shape[axis] = slice_dim
+            except (TypeError, IndexError):
+                return outputs
+        outputs[i].shape = out_shape
     return outputs
     return outputs
 
 
 
 
@@ -941,7 +952,7 @@ def stack_spec(args, inputs, outputs):
     out_shape = None
     out_shape = None
     for input in inputs:
     for input in inputs:
         if out_shape is None and input.shape is not None:
         if out_shape is None and input.shape is not None:
-            out_shape = input.shape[:]
+            out_shape = list(input.shape[:])
     try:
     try:
         for i in range(len(out_shape)):
         for i in range(len(out_shape)):
             for input in inputs:
             for input in inputs:
@@ -971,7 +982,7 @@ def tile_spec(args, inputs, outputs):
     repeats = args['repeats']
     repeats = args['repeats']
     if repeats is not None:
     if repeats is not None:
         try:
         try:
-            out_shape = inputs[0].shape[:]
+            out_shape = list(inputs[0].shape[:])
             for i, size in enumerate(repeats):
             for i, size in enumerate(repeats):
                 if i < len(out_shape):
                 if i < len(out_shape):
                     try:
                     try:
@@ -993,9 +1004,10 @@ def transpose_spec(args, inputs, outputs):
     try:
     try:
         if perm is None:
         if perm is None:
             perm = list(range(((len(inputs[0].shape)) - 1), -1, -1))
             perm = list(range(((len(inputs[0].shape)) - 1), -1, -1))
-        outputs[0].shape = inputs[0].shape[:]
+        out_shape = list(inputs[0].shape[:])
         for i, axis in enumerate(perm):
         for i, axis in enumerate(perm):
-            outputs[0].shape[i] = inputs[0].shape[axis]
+            out_shape[i] = inputs[0].shape[axis]
+        outputs[0].shape = out_shape
     except (TypeError, IndexError):
     except (TypeError, IndexError):
         outputs[0].shape = None
         outputs[0].shape = None
     return outputs
     return outputs

dragon/python/core/autograph/tensor.py

@@ -27,17 +27,17 @@ from dragon.core.util import nest
 class Tensor(types.TensorMetaclass):
 class Tensor(types.TensorMetaclass):
     """Tensor abstraction for graph executing."""
     """Tensor abstraction for graph executing."""
 
 
-    def __init__(self, name=None, shape=None, dtype=None):
+    def __init__(self, shape=None, dtype=None, name=None):
         """Create a ``Tensor``.
         """Create a ``Tensor``.
 
 
         Parameters
         Parameters
         ----------
         ----------
-        name : str, optional
-            The optional tensor name.
-        shape : sequence, optional
+        shape : Sequence[int], optional
             The optional tensor shape.
             The optional tensor shape.
         dtype : str, optional
         dtype : str, optional
             The optional data type.
             The optional data type.
+        name : str, optional
+            The optional tensor name.
 
 
         """
         """
         self._op, self._grad = None, None
         self._op, self._grad = None, None
@@ -66,7 +66,7 @@ class Tensor(types.TensorMetaclass):
             The data type to set.
             The data type to set.
 
 
         """
         """
-        self._dtype = value
+        self._dtype = str(value) if value else value
 
 
     @property
     @property
     def id(self):
     def id(self):
@@ -131,8 +131,8 @@ class Tensor(types.TensorMetaclass):
 
 
         Returns
         Returns
         -------
         -------
-        Sequence[int]
-            The shape.
+        Tuple[int]
+            The tensor shape.
 
 
         """
         """
         return self._shape
         return self._shape
@@ -150,10 +150,9 @@ class Tensor(types.TensorMetaclass):
         if value is not None:
         if value is not None:
             if not nest.is_sequence(value):
             if not nest.is_sequence(value):
                 raise TypeError(
                 raise TypeError(
-                    'The <shape> should be a Sequence. '
-                    'Got {}.'.format(type(value))
-                )
-            self._shape = nest.flatten(value)
+                    'The <shape> should be a sequence. Got {}.'
+                    .format(type(value).__name__))
+            self._shape = tuple(nest.flatten(value))
         else:
         else:
             self._shape = value
             self._shape = value
 
 
@@ -475,7 +474,7 @@ class Tensor(types.TensorMetaclass):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        item : Union[int, slice, dragon.Tensor]
+        item : Union[slice, int, dragon.Tensor]
             The index.
             The index.
 
 
         Returns
         Returns
@@ -643,7 +642,7 @@ class Tensor(types.TensorMetaclass):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        key : Union[int, slice, dragon.Tensor]
+        key : Union[slice, int, dragon.Tensor]
             The index.
             The index.
         value : Union[dragon.Tensor, number]
         value : Union[dragon.Tensor, number]
             The value to set.
             The value to set.
@@ -685,6 +684,6 @@ class TensorRef(object):
     """Create a reference not involved with name scope."""
     """Create a reference not involved with name scope."""
 
 
     def __new__(cls, name, shape=None, dtype=None):
     def __new__(cls, name, shape=None, dtype=None):
-        tensor = Tensor('', shape=shape, dtype=dtype)
-        tensor._name = name
-        return tensor
+        tensor_ref = Tensor(shape=shape, dtype=dtype, name='')
+        tensor_ref._name = name
+        return tensor_ref

dragon/python/core/eager/tensor.py

@@ -144,15 +144,15 @@ class EagerTensor(Tensor):
 
 
     @property
     @property
     def shape(self):
     def shape(self):
-        """Return the shape of this tensor.
+        """Return tensor shape.
 
 
         Returns
         Returns
         -------
         -------
-        Sequence[int]
-            The shape.
+        Tuple[int]
+            The tensor shape.
 
 
         """
         """
-        return self._impl.dims
+        return tuple(self._impl.dims)
 
 
     @shape.setter
     @shape.setter
     def shape(self, value):
     def shape(self, value):
@@ -451,7 +451,7 @@ class EagerTensor(Tensor):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        item : Union[int, slice, dragon.EagerTensor]
+        item : Union[slice, int, dragon.EagerTensor]
             The index.
             The index.
 
 
         Returns
         Returns
@@ -668,7 +668,7 @@ class EagerTensor(Tensor):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        key : Union[int, slice, dragon.EagerTensor]
+        key : Union[slice, int, dragon.EagerTensor]
             The index.
             The index.
         value : Union[dragon.EagerTensor, number]
         value : Union[dragon.EagerTensor, number]
             The value to set.
             The value to set.

dragon/python/core/framework/proto_util.py

@@ -36,7 +36,7 @@ if sys.version_info >= (3, 0):
             argument.i = value
             argument.i = value
         elif type(value) is bytes:
         elif type(value) is bytes:
             argument.s = value
             argument.s = value
-        elif type(value) is str:
+        elif isinstance(value, str):
             argument.s = str.encode(value)
             argument.s = str.encode(value)
         elif isinstance(value, Message):
         elif isinstance(value, Message):
             argument.s = value.SerializeToString()
             argument.s = value.SerializeToString()
@@ -63,7 +63,7 @@ else:
             argument.f = value
             argument.f = value
         elif type(value) in (bool, int, long, numpy.int64):
         elif type(value) in (bool, int, long, numpy.int64):
             argument.i = value
             argument.i = value
-        elif type(value) is str:
+        elif isinstance(value, str):
             argument.s = value
             argument.s = value
         elif type(value) is unicode:
         elif type(value) is unicode:
             argument.s = str(value)
             argument.s = str(value)

dragon/python/core/framework/tensor_spec.py

-# ------------------------------------------------------------
-# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
-#
-# Licensed under the BSD 2-Clause License.
-# You should have received a copy of the BSD 2-Clause License
-# along with the software. If not, See,
-#
-#    <https://opensource.org/licenses/BSD-2-Clause>
-#
-# ------------------------------------------------------------
-"""Structure to represent a tensor."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-from dragon.core.util import nest
-
-
-class TensorSpec(object):
-    """Spec to describe properties of a tensor."""
-
-    def __init__(self, shape, dtype='float32', name=None):
-        """Create a TensorSpec.
-
-        Parameters
-        ----------
-        shape : Sequence[int], required
-            The dimensions.
-        dtype : str, optional, default='float32'
-            The optional data type.
-        name : str, optional
-            The optional name.
-
-        """
-        self._shape, self._dtype, self._name = shape, dtype, name
-        if shape is not None:
-            self._shape = nest.flatten(shape)
-
-    @property
-    def dtype(self):
-        """Return the data type.
-
-        Returns
-        -------
-        str
-            The data type.
-
-        """
-        return self._dtype
-
-    @property
-    def name(self):
-        """Return the spec name.
-
-        Returns
-        -------
-        str
-            The spec name.
-
-        """
-        return self._name
-
-    @property
-    def shape(self):
-        """Return the dimensions.
-
-        Returns
-        -------
-        Sequence[int]
-            The dimensions.
-
-        """
-        return self._shape
-
-    def is_compatible_with(self, spec_or_tensor):
-        """Return a bool indicating whether given the spec is compatible.
-
-        Returns
-        -------
-        bool
-            **True** if ``shape`` and ``dtype`` are
-            both compatible otherwise **False**.
-
-        """
-        def dtype_is_compatible_with(spec_or_tensor):
-            return self._dtype == spec_or_tensor.dtype
-
-        def shape_is_compatible_with(spec_or_tensor):
-            shape = spec_or_tensor.shape
-            if self._shape is not None and shape is not None:
-                if len(self._shape) != len(shape):
-                    return False
-            for x_dim, y_dim in zip(self._shape, shape):
-                if x_dim != y_dim:
-                    return False
-            return True
-
-        return \
-            dtype_is_compatible_with(spec_or_tensor) and \
-            shape_is_compatible_with(spec_or_tensor)

dragon/python/core/framework/workspace.py

@@ -180,7 +180,7 @@ class Workspace(backend.Workspace):
 
 
         ```python
         ```python
         # Define a named tensor to feed
         # Define a named tensor to feed
-        x = dragon.Tensor('x')
+        x = dragon.Tensor(name='x')
         dragon.get_workspace().feed_tensor(x, 0)
         dragon.get_workspace().feed_tensor(x, 0)
 
 
         # Feed by specifying a tensor name
         # Feed by specifying a tensor name

dragon/python/core/ops/rnn_ops.py

@@ -18,7 +18,7 @@ import itertools
 import numpy
 import numpy
 import warnings
 import warnings
 
 
-from dragon.core.autograph.tensor import Tensor
+from dragon.core.autograph.tensor import TensorRef
 from dragon.core.eager import context
 from dragon.core.eager import context
 from dragon.core.eager.tensor import EagerTensor
 from dragon.core.eager.tensor import EagerTensor
 from dragon.core.framework import config
 from dragon.core.framework import config
@@ -191,7 +191,7 @@ class RNNModule(object):
             self._weights_count += int(numpy.prod(shape))
             self._weights_count += int(numpy.prod(shape))
         # Create the flat float32 weights.
         # Create the flat float32 weights.
         self._weights = EagerTensor(shape=[self._weights_count], trainable=True)
         self._weights = EagerTensor(shape=[self._weights_count], trainable=True)
-        self._weights_ref = Tensor(self._weights.name)
+        self._weights_ref = TensorRef(self._weights.name)
 
 
     def _uniform_init(self, shape, dtype='float32'):
     def _uniform_init(self, shape, dtype='float32'):
         stdv = 1. / numpy.sqrt(self.hidden_size)
         stdv = 1. / numpy.sqrt(self.hidden_size)

dragon/python/core/ops/tensorbind_eager.py

@@ -146,7 +146,7 @@ def getitem(self, item):
 
 
     Parameters
     Parameters
     ----------
     ----------
-    item : Union[int, slice, dragon.EagerTensor]
+    item : Union[slice, int, dragon.EagerTensor]
         The index.
         The index.
 
 
     Returns
     Returns
@@ -490,7 +490,7 @@ def setitem(self, key, value):
 
 
     Parameters
     Parameters
     ----------
     ----------
-    key : Union[int, slice, dragon.EagerTensor]
+    key : Union[slice, int, dragon.EagerTensor]
         The index.
         The index.
     value : Union[dragon.EagerTensor, number]
     value : Union[dragon.EagerTensor, number]
         The value to set.
         The value to set.

dragon/python/core/ops/tensorbind_symbol.py

@@ -121,7 +121,7 @@ def getitem(self, item):
 
 
     Parameters
     Parameters
     ----------
     ----------
-    item : Union[int, slice, dragon.Tensor]
+    item : Union[slice, int, dragon.Tensor]
         The index.
         The index.
 
 
     Returns
     Returns
@@ -324,7 +324,7 @@ def setitem(self, key, value):
 
 
     Parameters
     Parameters
     ----------
     ----------
-    key : Union[int, slice, dragon.Tensor]
+    key : Union[slice, int, dragon.Tensor]
         The index.
         The index.
     value : Union[dragon.Tensor, number]
     value : Union[dragon.Tensor, number]
         The value to set.
         The value to set.

tensorflow/__init__.py

@@ -58,8 +58,8 @@ from dragon.vm.tensorflow.core.framework.dtypes import variant
 from dragon.vm.tensorflow.core.framework.ops import convert_to_tensor
 from dragon.vm.tensorflow.core.framework.ops import convert_to_tensor
 from dragon.vm.tensorflow.core.framework.ops import device
 from dragon.vm.tensorflow.core.framework.ops import device
 from dragon.vm.tensorflow.core.framework.ops import name_scope
 from dragon.vm.tensorflow.core.framework.ops import name_scope
-from dragon.vm.tensorflow.core.framework.tensor_spec import TensorSpec
 from dragon.vm.tensorflow.core.framework.tensor_shape import TensorShape
 from dragon.vm.tensorflow.core.framework.tensor_shape import TensorShape
+from dragon.vm.tensorflow.core.framework.tensor_spec import TensorSpec
 from dragon.vm.tensorflow.core.module.module import Module
 from dragon.vm.tensorflow.core.module.module import Module
 from dragon.vm.tensorflow.core.ops.array_ops import broadcast_to
 from dragon.vm.tensorflow.core.ops.array_ops import broadcast_to
 from dragon.vm.tensorflow.core.ops.array_ops import concat
 from dragon.vm.tensorflow.core.ops.array_ops import concat

tensorflow/core/framework/constant_op.py

@@ -52,9 +52,9 @@ def constant(value, dtype=None, shape=None, name='Const'):
     """
     """
     if dtype is not None:
     if dtype is not None:
         if isinstance(value, numpy.ndarray):
         if isinstance(value, numpy.ndarray):
-            value = value.astype(str(dtype))
+            value = value.astype(dtype)
         else:
         else:
-            value = numpy.array(value, str(dtype))
+            value = numpy.array(value, dtype)
     else:
     else:
         if not isinstance(value, numpy.ndarray):
         if not isinstance(value, numpy.ndarray):
             value = numpy.array(value)
             value = numpy.array(value)

tensorflow/core/framework/dtypes.py

@@ -12,6 +12,7 @@
 #    <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/dtypes.py>
 #    <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/dtypes.py>
 #
 #
 # ------------------------------------------------------------
 # ------------------------------------------------------------
+"""Data type utilities."""
 
 
 from __future__ import absolute_import
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import division
@@ -45,8 +46,34 @@ DT_VARIANT = 21
 DT_UINT32 = 22
 DT_UINT32 = 22
 DT_UINT64 = 23
 DT_UINT64 = 23
 
 
+# Mappings between string names and enum values.
+_STRING_TO_ENUM = {
+    'float16': DT_HALF,
+    'float32': DT_FLOAT,
+    'float64': DT_DOUBLE,
+    'int8': DT_INT8,
+    'uint8': DT_UINT8,
+    'int16': DT_INT16,
+    'uint16': DT_UINT16,
+    'int32': DT_INT32,
+    'uint32': DT_UINT32,
+    'int64': DT_INT64,
+    'uint64': DT_UINT64,
+    'string': DT_STRING,
+    'complex64': DT_COMPLEX64,
+    'complex128': DT_COMPLEX128,
+    'bool': DT_BOOL,
+    'qint8': DT_QINT8,
+    'quint8': DT_QUINT8,
+    'qint16': DT_QINT16,
+    'quint16': DT_QUINT16,
+    'qint32': DT_QINT32,
+    'bfloat16': DT_BFLOAT16,
+    'variant': DT_VARIANT,
+}
+
 
 
-class DType(object):
+class DType(str):
     """The basic data type.
     """The basic data type.
 
 
     Following data types are defined:
     Following data types are defined:
@@ -97,110 +124,251 @@ class DType(object):
 
 
     """
     """
 
 
-    def __init__(self, type_enum):
+    def __init__(self, value):
         """Create a ``DType``.
         """Create a ``DType``.
 
 
         Parameters
         Parameters
         ----------
         ----------
-        type_enum : DataType
-            The ``DataType`` value.
+        value : str
+            The string name.
 
 
         """
         """
-        type_enum = int(type_enum)
-        if type_enum == DT_INVALID:
-            raise TypeError('<type_enum> is not a valid DataType.')
-        self._type_enum = type_enum
+        super(DType, self).__init__()
+        self._name = value
+        self._type_enum = _STRING_TO_ENUM[value]
 
 
     @property
     @property
-    def base_dtype(self):
-        return self
+    def as_numpy_dtype(self):
+        """Return as the number of numpy data type.
+
+        Returns
+        -------
+        type
+            The data type number.
+
+        """
+        return _TF_TO_NP[self._type_enum]
 
 
     @property
     @property
-    def real_dtype(self):
-        base = self.base_dtype
-        if base == complex64:
-            return float32
-        elif base == complex128:
-            return float64
-        else:
+    def as_datatype_enum(self):
+        """Return as the enum value of data type.
+
+        Returns
+        -------
+        int
+            The enum value.
+
+        """
+        return self._type_enum
+
+    @property
+    def base_dtype(self):
+        """Return the non-referenced data type.
+
+        Returns
+        -------
+        dragon.vm.tensorflow.dtypes.DType
+            The data type.
+
+        """
         return self
         return self
 
 
     @property
     @property
     def is_numpy_compatible(self):
     def is_numpy_compatible(self):
+        """Return whether this data type is compatible with numpy.
+
+        Returns
+        -------
+        bool
+            **True** if compatible otherwise **False**.
+
+        """
         return self._type_enum in _TF_TO_NP
         return self._type_enum in _TF_TO_NP
 
 
     @property
     @property
-    def as_numpy_dtype(self):
-        return _TF_TO_NP[self._type_enum]
+    def is_bool(self):
+        """Return whether this is a boolean type.
+
+        Returns
+        -------
+        bool
+            **True** if this is a boolean type otherwise **False**.
+
+        """
+        return self.base_dtype == bool
 
 
     @property
     @property
-    def as_datatype_enum(self):
-        return self._type_enum
+    def is_complex(self):
+        """Return whether this is a complex type.
+
+        Returns
+        -------
+        bool
+            **True** if this is a complex type otherwise **False**.
+
+        """
+        return self.base_dtype in (complex64, complex128)
 
 
     @property
     @property
-    def is_bool(self):
-        return self.base_dtype == bool
+    def is_floating(self):
+        """Return whether this is a floating type.
+
+        Returns
+        -------
+        bool
+            **True** if this is a floating type otherwise **False**.
+
+        """
+        return (self.is_numpy_compatible and
+                issubclass(self.as_numpy_dtype, np.floating))
 
 
     @property
     @property
     def is_integer(self):
     def is_integer(self):
+        """Return whether this is a integer type.
+
+        Returns
+        -------
+        bool
+            **True** if this is a integer type otherwise **False**.
+
+        """
         return (self.is_numpy_compatible and
         return (self.is_numpy_compatible and
                 not self.is_quantized and
                 not self.is_quantized and
                 issubclass(self.as_numpy_dtype, np.integer))
                 issubclass(self.as_numpy_dtype, np.integer))
 
 
     @property
     @property
-    def is_floating(self):
-        return self.is_numpy_compatible and \
-            issubclass(self.as_numpy_dtype, np.floating)
+    def is_quantized(self):
+        """Return whether this is a quantized type.
 
 
-    @property
-    def is_complex(self):
-        return self.base_dtype in (complex64, complex128)
+        Returns
+        -------
+        bool
+            **True** if this is a quantized type otherwise **False**.
 
 
-    @property
-    def is_quantized(self):
+        """
         return self.base_dtype in [qint8, quint8, qint16, quint16, qint32, bfloat16]
         return self.base_dtype in [qint8, quint8, qint16, quint16, qint32, bfloat16]
 
 
     @property
     @property
     def is_unsigned(self):
     def is_unsigned(self):
+        """Return whether this is an unsigned type.
+
+        Returns
+        -------
+        bool
+            **True** if this is an unsigned type otherwise **False**.
+
+        """
         try:
         try:
             return self.min == 0
             return self.min == 0
         except TypeError:
         except TypeError:
             return False
             return False
 
 
     @property
     @property
-    def min(self):
+    def limits(self, clip_negative=True):
+        """Return the numerical limits.
+
+        Parameters
+        ----------
+        clip_negative : bool, optional, default=True
+            **True** to return positive limits only.
+
+        Returns
+        -------
+        Tuple[number, number]
+            The limits.
+
+        """
+        min, max = dtype_range[self.as_numpy_dtype]
+        if clip_negative:
+            min = 0
+        return min, max
+
+    @property
+    def max(self):
+        """Return the max representable value.
+
+        Returns
+        -------
+        number
+            The max representable value.
+
+        """
         if (self.is_quantized or self.base_dtype in
         if (self.is_quantized or self.base_dtype in
                 (bool, string, complex64, complex128)):
                 (bool, string, complex64, complex128)):
-            raise TypeError("Cannot find minimum value of %s." % self)
+            raise TypeError('Cannot find maximum value of %s.' % self)
         try:
         try:
-            return np.finfo(self.as_numpy_dtype()).min
+            return np.finfo(self.as_numpy_dtype()).max
         except (TypeError, ValueError):
         except (TypeError, ValueError):
             try:
             try:
-                return np.iinfo(self.as_numpy_dtype()).min
+                return np.iinfo(self.as_numpy_dtype()).max
             except (TypeError, ValueError):
             except (TypeError, ValueError):
-                raise TypeError("Cannot find minimum value of %s." % self)
+                raise TypeError('Cannot find maximum value of %s.' % self)
 
 
     @property
     @property
-    def max(self):
+    def min(self):
+        """Return the min representable value.
+
+        Returns
+        -------
+        number
+            The min representable value
+
+        """
         if (self.is_quantized or self.base_dtype in
         if (self.is_quantized or self.base_dtype in
                 (bool, string, complex64, complex128)):
                 (bool, string, complex64, complex128)):
-            raise TypeError("Cannot find maximum value of %s." % self)
+            raise TypeError("Cannot find minimum value of %s." % self)
         try:
         try:
-            return np.finfo(self.as_numpy_dtype()).max
+            return np.finfo(self.as_numpy_dtype()).min
         except (TypeError, ValueError):
         except (TypeError, ValueError):
             try:
             try:
-                return np.iinfo(self.as_numpy_dtype()).max
+                return np.iinfo(self.as_numpy_dtype()).min
             except (TypeError, ValueError):
             except (TypeError, ValueError):
-                raise TypeError("Cannot find maximum value of %s." % self)
+                raise TypeError("Cannot find minimum value of %s." % self)
 
 
     @property
     @property
-    def limits(self, clip_negative=True):
-        min, max = dtype_range[self.as_numpy_dtype]
-        if clip_negative:
-            min = 0
-        return min, max
+    def name(self):
+        """Return the type name.
+
+        Returns
+        -------
+        str
+            The type name.
+
+        """
+        return self._name
+
+    @property
+    def real_dtype(self):
+        """Return the data type of real part.
+
+        Returns
+        -------
+        dragon.vm.tensorflow.dtypes.DType
+            The data type of real part.
+
+        """
+        base = self.base_dtype
+        if base == complex64:
+            return float32
+        elif base == complex128:
+            return float64
+        else:
+            return self
 
 
     def is_compatible_with(self, other):
     def is_compatible_with(self, other):
+        """Return whether this data type can be converted as the other.
+
+        Parameters
+        ----------
+        other : dragon.vm.tensorflow.dtypes.DType
+            The referring data type.
+
+        Returns
+        -------
+        bool
+            **True** if compatible otherwise **False**.
+
+        """
         other = as_dtype(other)
         other = as_dtype(other)
         return self._type_enum in (
         return self._type_enum in (
             other.as_datatype_enum, other.base_dtype.as_datatype_enum)
             other.as_datatype_enum, other.base_dtype.as_datatype_enum)
@@ -217,16 +385,11 @@ class DType(object):
     def __ne__(self, other):
     def __ne__(self, other):
         return not self.__eq__(other)
         return not self.__eq__(other)
 
 
-    @property
-    def name(self):
-        """Returns the string name for this `DType`."""
-        return _TYPE_TO_STRING[self._type_enum]
-
     def __int__(self):
     def __int__(self):
         return self._type_enum
         return self._type_enum
 
 
     def __str__(self):
     def __str__(self):
-        return self.name
+        return self._name
 
 
     def __repr__(self):
     def __repr__(self):
         return "tf." + self.name
         return "tf." + self.name
@@ -235,8 +398,9 @@ class DType(object):
         return self._type_enum
         return self._type_enum
 
 
 
 
-# Define data type range of numpy dtype
-dtype_range = {np.bool_: (False, True),
+# Range of numpy dtype
+dtype_range = {
+    np.bool_: (False, True),
     np.bool8: (False, True),
     np.bool8: (False, True),
     np.uint8: (0, 255),
     np.uint8: (0, 255),
     np.uint16: (0, 65535),
     np.uint16: (0, 65535),
@@ -247,58 +411,33 @@ dtype_range = {np.bool_: (False, True),
     np.int32: (-2 ** 31, 2 ** 31 - 1),
     np.int32: (-2 ** 31, 2 ** 31 - 1),
     np.uint32: (0, 2 ** 32 - 1),
     np.uint32: (0, 2 ** 32 - 1),
     np.float32: (-1, 1),
     np.float32: (-1, 1),
-               np.float64: (-1, 1)}
-
-
-# Define standard wrappers for the DataType enum.
-float16 = DType(DT_HALF)
-half = float16
-float32 = DType(DT_FLOAT)
-float64 = DType(DT_DOUBLE)
-double = float64
-int32 = DType(DT_INT32)
-uint8 = DType(DT_UINT8)
-uint16 = DType(DT_UINT16)
-uint64 = DType(DT_UINT32)
-uint32 = DType(DT_UINT64)
-int16 = DType(DT_INT16)
-int8 = DType(DT_INT8)
-string = DType(DT_STRING)
-complex64 = DType(DT_COMPLEX64)
-complex128 = DType(DT_COMPLEX128)
-int64 = DType(DT_INT64)
-bool = DType(DT_BOOL)
-qint8 = DType(DT_QINT8)
-quint8 = DType(DT_QUINT8)
-qint16 = DType(DT_QINT16)
-quint16 = DType(DT_QUINT16)
-qint32 = DType(DT_QINT32)
-bfloat16 = DType(DT_BFLOAT16)
-variant = DType(DT_VARIANT)
-
-# Standard mappings between DataType values and string names.
-_TYPE_TO_STRING = {
-    DT_HALF: "float16",
-    DT_FLOAT: "float32",
-    DT_DOUBLE: "float64",
-    DT_INT32: "int32",
-    DT_UINT8: "uint8",
-    DT_UINT16: "uint16",
-    DT_INT16: "int16",
-    DT_INT8: "int8",
-    DT_STRING: "string",
-    DT_COMPLEX64: "complex64",
-    DT_COMPLEX128: "complex128",
-    DT_INT64: "int64",
-    DT_BOOL: "bool",
-    DT_QINT8: "qint8",
-    DT_QUINT8: "quint8",
-    DT_QINT16: "qint16",
-    DT_QUINT16: "quint16",
-    DT_QINT32: "qint32",
-    DT_BFLOAT16: "bfloat16",
+    np.float64: (-1, 1),
 }
 }
 
 
+# Define standard wrappers for the string name.
+float16 = half = DType('float16')
+float32 = DType('float32')
+float64 = double = DType('float64')
+int32 = DType('int32')
+uint8 = DType('uint8')
+uint16 = DType('uint16')
+uint64 = DType('uint32')
+uint32 = DType('uint64')
+int16 = DType('int16')
+int8 = DType('int8')
+string = DType('string')
+complex64 = DType('complex64')
+complex128 = DType('complex128')
+int64 = DType('int64')
+bool = DType('bool')
+qint8 = DType('qint8')
+quint8 = DType('quint8')
+qint16 = DType('qint16')
+quint16 = DType('quint16')
+qint32 = DType('qint32')
+bfloat16 = DType('bfloat16')
+variant = DType('variant')
+
 # Numpy representation for quantized dtypes.
 # Numpy representation for quantized dtypes.
 _np_qint8 = np.dtype([("qint8", np.int8)])
 _np_qint8 = np.dtype([("qint8", np.int8)])
 _np_quint8 = np.dtype([("quint8", np.uint8)])
 _np_quint8 = np.dtype([("quint8", np.uint8)])
@@ -379,7 +518,7 @@ _INTERN_TABLE = {
 }
 }
 
 
 _STRING_TO_TF = {
 _STRING_TO_TF = {
-    value: _INTERN_TABLE[key] for key, value in _TYPE_TO_STRING.items()
+    key: _INTERN_TABLE[value] for key, value in _STRING_TO_ENUM.items()
 }
 }
 
 
 _ANY_TO_TF = {}
 _ANY_TO_TF = {}
@@ -404,16 +543,13 @@ def as_dtype(type_value):
     """
     """
     if isinstance(type_value, DType):
     if isinstance(type_value, DType):
         return type_value
         return type_value
-
     if isinstance(type_value, np.dtype):
     if isinstance(type_value, np.dtype):
         try:
         try:
             return _NP_TO_TF[type_value.type]
             return _NP_TO_TF[type_value.type]
         except KeyError:
         except KeyError:
             pass
             pass
-
     try:
     try:
         return _ANY_TO_TF[type_value]
         return _ANY_TO_TF[type_value]
     except KeyError:
     except KeyError:
         pass
         pass
-
     raise TypeError("Cannot convert value %r to a TensorFlow DType." % type_value)
     raise TypeError("Cannot convert value %r to a TensorFlow DType." % type_value)

tensorflow/core/framework/framework_lib.py

@@ -18,5 +18,4 @@ from dragon.vm.tensorflow.core.framework.constant_op import *
 from dragon.vm.tensorflow.core.framework.dtypes import *
 from dragon.vm.tensorflow.core.framework.dtypes import *
 from dragon.vm.tensorflow.core.framework.ops import device
 from dragon.vm.tensorflow.core.framework.ops import device
 from dragon.vm.tensorflow.core.framework.ops import convert_to_tensor
 from dragon.vm.tensorflow.core.framework.ops import convert_to_tensor
-from dragon.vm.tensorflow.core.framework.tensor_shape import Dimension
 from dragon.vm.tensorflow.core.framework.tensor_shape import TensorShape
 from dragon.vm.tensorflow.core.framework.tensor_shape import TensorShape

tensorflow/core/framework/tensor_shape.py

@@ -12,127 +12,87 @@
 #    <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/tensor_shape.py>
 #    <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/framework/tensor_shape.py>
 #
 #
 # ------------------------------------------------------------
 # ------------------------------------------------------------
+"""Tensor shape utilities."""
 
 
 from __future__ import absolute_import
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import division
 from __future__ import print_function
 from __future__ import print_function
 
 
-from dragon.core.autograph.tensor import Tensor
-from dragon.core.eager.tensor import EagerTensor
 
 
+class TensorShape(tuple):
+    """Represent the a sequence of dimensions."""
 
 
-class Dimension(object):
-    def __init__(self, value):
-        if value is None:
-            self._value = None
-        else:
-            self._value = int(value)
-            if self._value < 0:
-                raise ValueError("Dimension %d must be >= 0" % self._value)
+    def __init__(self, dims):
+        """Create a ``TensorShape``.
 
 
-    @property
-    def value(self):
-        return self._value
+        Parameters
+        ----------
+        dims : Sequence[int]
+            The dimensions.
 
 
-    def __repr__(self):
-        return "Dimension(%s)" % repr(self._value)
+        """
+        super(TensorShape, self).__init__()
 
 
-    def __str__(self):
-        value = self._value
-        return "?" if value is None else str(value)
-
-    def __eq__(self, other):
-        try:
-            other = as_dimension(other)
-        except (TypeError, ValueError):
-            return NotImplemented
-        if self._value is None or other.value is None:
-            return None
-        return self._value == other.value
-
-    def __ne__(self, other):
-        try:
-            other = as_dimension(other)
-        except (TypeError, ValueError):
-            return NotImplemented
-        if self._value is None or other.value is None:
-            return None
-        return self._value != other.value
-
-    def __int__(self):
-        return self._value
-
-
-def as_dimension(value):
-    if isinstance(value, Dimension):
-        return value
-    else:
-        return Dimension(value)
+    @property
+    def dims(self):
+        """Return the list of dimensions.
 
 
+        Returns
+        -------
+        List[int]
+            The dimensions.
 
 
-class TensorShape(object):
-    def __init__(self, dims):
-        if dims is None:
-            self._dims = None
-        elif isinstance(dims, TensorShape):
-            self._dims = dims.dims
-        else:
-            try:
-                dims_iter = iter(dims)
-            except TypeError:
-                self._dims = [as_dimension(dims)]
-            else:
-                self._dims = [as_dimension(d) for d in dims_iter]
+        """
+        return list(self)
 
 
     @property
     @property
-    def dims(self):
-        return self._dims
+    def ndims(self):
+        """Return the number of dimensions.
+
+        Deprecated. See ``TensorShape.rank``.
+
+        Returns
+        -------
+        int
+            The number of dimensions.
+
+        """
+        return len(self)
 
 
     @property
     @property
-    def ndims(self):
-        if self._dims is None:
-            return None
-        else:
-            return len(self._dims)
+    def rank(self):
+        """Return the rank of shape.
+
+        Returns
+        -------
+        int
+            The rank.
+
+        """
+        return len(self)
 
 
     def as_list(self):
     def as_list(self):
-        if self._dims is None:
-            raise ValueError("as_list() is not defined on an unknown TensorShape.")
-        return [dim.value for dim in self._dims]
+        """Return the list of dimensions.
+
+        Returns
+        -------
+        List[int]
+            The dimensions.
+
+        """
+        return list(self)
 
 
     def __repr__(self):
     def __repr__(self):
-        return "TensorShape(%r)" % self._dims
+        return "TensorShape({})".format(list(self))
 
 
     def __str__(self):
     def __str__(self):
-        if self.ndims is None:
-            return "<unknown>"
-        elif self.ndims == 1:
-            return "(%s,)" % self._dims[0]
+        if self.ndims == 1:
+            return "(%s,)" % self.dims[0]
         else:
         else:
-            return "(%s)" % ", ".join(str(d) for d in self._dims)
+            return "(%s)" % ", ".join(str(d) for d in self.dims)
 
 
     def __getitem__(self, key):
     def __getitem__(self, key):
-        if self._dims is not None:
         if isinstance(key, slice):
         if isinstance(key, slice):
-                return TensorShape(self._dims[key])
+            return TensorShape(self.dims[key])
         else:
         else:
-                return self._dims[key]
-        else:
-            return Dimension(None)
-
-
-def dimension_value(dimension):
-    """Return the value of specified dimension."""
-    if isinstance(dimension, Dimension):
-        return dimension.value
-    return dimension
-
-
-def get_shape(self):
-    """Construct the shape descriptor."""
-    return TensorShape(self.shape)
-
-
-# The Monkey Patching.
-EagerTensor.get_shape = get_shape
-Tensor.get_shape = get_shape
+            return self.dims[key]

tensorflow/core/framework/tensor_spec.py

@@ -17,16 +17,26 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import division
 from __future__ import print_function
 from __future__ import print_function
 
 
-from dragon.core.framework import tensor_spec
 from dragon.vm.tensorflow.core.framework import dtypes
 from dragon.vm.tensorflow.core.framework import dtypes
 from dragon.vm.tensorflow.core.framework import tensor_shape
 from dragon.vm.tensorflow.core.framework import tensor_shape
 
 
 
 
-class TensorSpec(tensor_spec.TensorSpec):
+class TensorSpec(object):
     """Spec to describe properties of a tensor."""
     """Spec to describe properties of a tensor."""
 
 
-    def __init__(self, shape, dtype=dtypes.float32, name=None):
-        """Create a ``TensorSpec``."""
+    def __init__(self, shape, dtype='float32', name=None):
+        """Create a TensorSpec.
+
+        Parameters
+        ----------
+        shape : Sequence[int], required
+            The dimensions.
+        dtype : str, optional, default='float32'
+            The optional data type.
+        name : str, optional
+            The optional name.
+
+        """
         self._shape = tensor_shape.TensorShape(shape)
         self._shape = tensor_shape.TensorShape(shape)
         try:
         try:
             self._shape_tuple = tuple(self._shape.as_list())
             self._shape_tuple = tuple(self._shape.as_list())
@@ -45,7 +55,7 @@ class TensorSpec(tensor_spec.TensorSpec):
             The data type.
             The data type.
 
 
         """
         """
-        return self._dtype.name
+        return str(self._dtype)
 
 
     @property
     @property
     def name(self):
     def name(self):
@@ -70,3 +80,29 @@ class TensorSpec(tensor_spec.TensorSpec):
 
 
         """
         """
         return self._shape.as_list()
         return self._shape.as_list()
+
+    def is_compatible_with(self, spec_or_tensor):
+        """Return a bool whether given the spec is compatible.
+
+        Returns
+        -------
+        bool
+            **True** if compatible otherwise **False**.
+
+        """
+        def dtype_is_compatible_with(spec_or_tensor):
+            return self.dtype == spec_or_tensor.dtype
+
+        def shape_is_compatible_with(spec_or_tensor):
+            shape = spec_or_tensor.shape
+            if self._shape is not None and shape is not None:
+                if len(self.shape) != len(shape):
+                    return False
+            for x_dim, y_dim in zip(self.shape, shape):
+                if x_dim != y_dim:
+                    return False
+            return True
+
+        return \
+            dtype_is_compatible_with(spec_or_tensor) and \
+            shape_is_compatible_with(spec_or_tensor)

tensorflow/core/keras/engine/input_layer.py

@@ -43,8 +43,8 @@ def Input(
     x = tf.keras.Input(shape=(8,), batch_size=8, dtype='float32')
     x = tf.keras.Input(shape=(8,), batch_size=8, dtype='float32')
 
 
     # Create a placeholder aliasing an existing tensor
     # Create a placeholder aliasing an existing tensor
-    x = dragon.Tensor('x', shape=(8,), dtype='float32').constant()
-    xx = tf.keras.Input(tensor=x)
+    x = dragon.Tensor(shape=(8,), dtype='float32').constant()
+    y = tf.keras.Input(tensor=x)
     ```
     ```
 
 
     Parameters
     Parameters
@@ -69,38 +69,29 @@ def Input(
     if 'batch_shape' in kwargs:
     if 'batch_shape' in kwargs:
         batch_shape = kwargs.pop('batch_shape')
         batch_shape = kwargs.pop('batch_shape')
         if shape and batch_shape:
         if shape and batch_shape:
-            raise ValueError(
-                'Specify <shape> or '
-                '<batch_shape>, not both.'
-            )
+            raise ValueError('Specify <shape> or <batch_shape>, not both.')
         shape = batch_shape
         shape = batch_shape
     else:
     else:
         if shape is not None:
         if shape is not None:
             shape = (batch_size,) + tuple(shape)
             shape = (batch_size,) + tuple(shape)
-
     if kwargs:
     if kwargs:
         raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
         raise ValueError('Unrecognized keyword arguments:', kwargs.keys())
-
     if dtype is None:
     if dtype is None:
         if tensor is not None:
         if tensor is not None:
             dtype = tensor.dtype
             dtype = tensor.dtype
         else:
         else:
             dtype = 'float32'
             dtype = 'float32'
-
     if shape is None:
     if shape is None:
         if tensor is None:
         if tensor is None:
             raise ValueError('Specify either <shape> or <tensor>.')
             raise ValueError('Specify either <shape> or <tensor>.')
         else:
         else:
             shape = tensor.shape
             shape = tensor.shape
-
     if isinstance(shape, tensor_shape.TensorShape):
     if isinstance(shape, tensor_shape.TensorShape):
         shape = tuple(shape.as_list())
         shape = tuple(shape.as_list())
     elif isinstance(shape, six.integer_types):
     elif isinstance(shape, six.integer_types):
         shape = (shape,)
         shape = (shape,)
-
     placeholder = array_ops.placeholder(
     placeholder = array_ops.placeholder(
         dtype=dtype, shape=shape, name=name if name else 'input')
         dtype=dtype, shape=shape, name=name if name else 'input')
     if tensor is not None:
     if tensor is not None:
         workspace.get_workspace().register_alias(tensor, placeholder.id)
         workspace.get_workspace().register_alias(tensor, placeholder.id)
-
     return placeholder
     return placeholder

tensorflow/core/keras/engine/input_spec.py

@@ -78,8 +78,7 @@ def assert_input_compatibility(input_spec, inputs, layer_name):
         raise ValueError(
         raise ValueError(
             'Layer ' + layer_name + ' expects ' +
             'Layer ' + layer_name + ' expects ' +
             str(len(input_spec)) + ' inputs, '
             str(len(input_spec)) + ' inputs, '
-            'but it received ' + str(len(inputs)) + ' input tensors.'
-        )
+            'but it received ' + str(len(inputs)) + ' input tensors.')
     # For each pair of input and spec.
     # For each pair of input and spec.
     for input_index, (x, spec) in enumerate(zip(inputs, input_spec)):
     for input_index, (x, spec) in enumerate(zip(inputs, input_spec)):
         if spec is None:
         if spec is None:

tensorflow/core/keras/layers/convolutional.py

@@ -72,13 +72,11 @@ class Conv(Layer):
     def build(self, input_shape):
     def build(self, input_shape):
         input_shape = tensor_shape.TensorShape(input_shape)
         input_shape = tensor_shape.TensorShape(input_shape)
         channel_axis = self._get_channel_axis()
         channel_axis = self._get_channel_axis()
-        if input_shape.dims[channel_axis].value is None:
+        if input_shape.dims[channel_axis] is None:
             raise ValueError(
             raise ValueError(
                 'The channel dimension of the input '
                 'The channel dimension of the input '
-                'should be determined, got None.'
-            )
+                'should be determined, got None.')
         input_dim = int(input_shape[channel_axis])
         input_dim = int(input_shape[channel_axis])
-
         # Assume that kernel is packed into NCHW format
         # Assume that kernel is packed into NCHW format
         # for computing the fans correctly
         # for computing the fans correctly
         if self.filters > 0:
         if self.filters > 0:
@@ -86,7 +84,6 @@ class Conv(Layer):
         else:
         else:
             self.filters = input_dim
             self.filters = input_dim
             kernel_shape = (input_dim, 1) + self.kernel_size
             kernel_shape = (input_dim, 1) + self.kernel_size
-
         self.kernel = self.add_weight(
         self.kernel = self.add_weight(
             name='kernel',
             name='kernel',
             shape=kernel_shape,
             shape=kernel_shape,
@@ -106,7 +103,6 @@ class Conv(Layer):
             )
             )
         else:
         else:
             self.bias = None
             self.bias = None
-
         self.built = True
         self.built = True
 
 
     def call(self, inputs):
     def call(self, inputs):
@@ -280,17 +276,15 @@ class Conv2DTranspose(Conv2D):
     def build(self, input_shape):
     def build(self, input_shape):
         input_shape = tensor_shape.TensorShape(input_shape)
         input_shape = tensor_shape.TensorShape(input_shape)
         channel_axis = self._get_channel_axis()
         channel_axis = self._get_channel_axis()
-        if input_shape.dims[channel_axis].value is None:
+        if input_shape.dims[channel_axis] is None:
             raise ValueError(
             raise ValueError(
                 'The channel dimension of the inputs '
                 'The channel dimension of the inputs '
                 'should be determined, got None.'
                 'should be determined, got None.'
             )
             )
         input_dim = int(input_shape[channel_axis])
         input_dim = int(input_shape[channel_axis])
-
         # Assume that kernel is packed into NCHW format,
         # Assume that kernel is packed into NCHW format,
         # for computing the fans correctly.
         # for computing the fans correctly.
         kernel_shape = (input_dim, self.filters) + self.kernel_size
         kernel_shape = (input_dim, self.filters) + self.kernel_size
-
         self.kernel = self.add_weight(
         self.kernel = self.add_weight(
             name='kernel',
             name='kernel',
             shape=kernel_shape,
             shape=kernel_shape,
@@ -310,7 +304,6 @@ class Conv2DTranspose(Conv2D):
             )
             )
         else:
         else:
             self.bias = None
             self.bias = None
-
         self.built = True
         self.built = True
 
 
     def call(self, inputs):
     def call(self, inputs):
@@ -320,11 +313,9 @@ class Conv2DTranspose(Conv2D):
             h_axis, w_axis = 2, 3
             h_axis, w_axis = 2, 3
         else:
         else:
             h_axis, w_axis = 1, 2
             h_axis, w_axis = 1, 2
-
         height, width = inputs_shape[h_axis], inputs_shape[w_axis]
         height, width = inputs_shape[h_axis], inputs_shape[w_axis]
         kernel_h, kernel_w = self.kernel_size
         kernel_h, kernel_w = self.kernel_size
         stride_h, stride_w = self.strides
         stride_h, stride_w = self.strides
-
         if self.output_padding is None:
         if self.output_padding is None:
             out_pad_h = out_pad_w = None
             out_pad_h = out_pad_w = None
         else:
         else:
@@ -349,7 +340,6 @@ class Conv2DTranspose(Conv2D):
             output_shape = (batch_size, self.filters, out_height, out_width)
             output_shape = (batch_size, self.filters, out_height, out_width)
         else:
         else:
             output_shape = (batch_size, out_height, out_width, self.filters)
             output_shape = (batch_size, out_height, out_width, self.filters)
-
         outputs = nn_ops.conv_transpose(
         outputs = nn_ops.conv_transpose(
             input=inputs,
             input=inputs,
             filters=self.kernel,
             filters=self.kernel,

tensorflow/core/keras/layers/core.py

@@ -83,20 +83,17 @@ class Dense(Layer):
         if not (dtype.is_floating or dtype.is_complex):
         if not (dtype.is_floating or dtype.is_complex):
             raise TypeError(
             raise TypeError(
                 'Unable to build `Dense` layer with non-floating point '
                 'Unable to build `Dense` layer with non-floating point '
-                'dtype %s' % (dtype,)
-            )
+                'dtype %s' % (dtype,))
         if self.input_dim is None:
         if self.input_dim is None:
             input_shape = tensor_shape.TensorShape(input_shape)
             input_shape = tensor_shape.TensorShape(input_shape)
-            if tensor_shape.dimension_value(input_shape[-1]) is None:
+            if input_shape[-1] is None:
                 raise ValueError(
                 raise ValueError(
                     'The last dimension of the inputs should be defined.\n'
                     'The last dimension of the inputs should be defined.\n'
-                    'Or you should specify <input_dim> in the constructor.'
-                )
-            last_dim = tensor_shape.dimension_value(input_shape[-1])
+                    'Or you should specify <input_dim> in the constructor.')
+            last_dim = input_shape[-1]
         else:
         else:
             last_dim = self.input_dim
             last_dim = self.input_dim
         self.input_spec = InputSpec(min_ndim=2, axes={-1: last_dim})
         self.input_spec = InputSpec(min_ndim=2, axes={-1: last_dim})
-
         self.kernel = self.add_weight(
         self.kernel = self.add_weight(
             'kernel',
             'kernel',
             shape=[last_dim, self.units],
             shape=[last_dim, self.units],

tensorflow/core/keras/layers/normalization.py

@@ -97,7 +97,7 @@ class BatchNormalization(Layer):
         input_shape = tensor_shape.TensorShape(input_shape)
         input_shape = tensor_shape.TensorShape(input_shape)
         if not input_shape.ndims:
         if not input_shape.ndims:
             raise ValueError('Input has undefined rank:', input_shape)
             raise ValueError('Input has undefined rank:', input_shape)
-        param_shape = [input_shape.dims[self.axis].value]
+        param_shape = [input_shape.dims[self.axis]]
         self.input_spec = InputSpec(
         self.input_spec = InputSpec(
             # Each layer should adapt to the:
             # Each layer should adapt to the:
             #  1) The number of dimensions.
             #  1) The number of dimensions.

tensorflow/core/ops/array_ops.py

@@ -217,12 +217,7 @@ def fill(dims, value=0, dtype=None, name=None):
             dtype = 'int32'
             dtype = 'int32'
         elif dtype == numpy.float64:
         elif dtype == numpy.float64:
             dtype = 'float32'
             dtype = 'float32'
-    return init_ops.fill(
-        shape=dims,
-        value=value,
-        dtype=str(dtype),
-        name=name,
-    )
+    return init_ops.fill(shape=dims, value=value, dtype=dtype, name=name)
 
 
 
 
 def gather(params, indices, axis=0, name=None):
 def gather(params, indices, axis=0, name=None):
@@ -315,7 +310,7 @@ def ones(shape, dtype='float32', name=None):
         A optional name for the operation.
         A optional name for the operation.
 
 
     """
     """
-    return init_ops.fill(shape, value=1, dtype=str(dtype), name=name)
+    return init_ops.fill(shape, value=1, dtype=dtype, name=name)
 
 
 
 
 def ones_like(input, dtype='float32', name=None):
 def ones_like(input, dtype='float32', name=None):
@@ -340,7 +335,7 @@ def ones_like(input, dtype='float32', name=None):
         A optional name for the operation.
         A optional name for the operation.
 
 
     """
     """
-    return init_ops.ones_like(input, dtype=str(dtype), name=name)
+    return init_ops.ones_like(input, dtype=dtype, name=name)
 
 
 
 
 def one_hot(
 def one_hot(
@@ -488,13 +483,13 @@ def placeholder(dtype=None, shape=None, name=None):
         workspace.get_workspace().unique_name(
         workspace.get_workspace().unique_name(
             context.get_name_scope() + name if name else 'Placeholder',
             context.get_name_scope() + name if name else 'Placeholder',
             suffix=':0', namespace='Tensor'),
             suffix=':0', namespace='Tensor'),
-        dtype=str(dtype) if dtype else dtype,
+        dtype=dtype if dtype else dtype,
         shape=shape,
         shape=shape,
     ).constant()
     ).constant()
 
 
 
 
 def reshape(tensor, shape, name=None):
 def reshape(tensor, shape, name=None):
-    r"""Change the dimensions of input.
+    """Change the dimensions of input.
 
 
     Examples:
     Examples:
 
 
@@ -527,7 +522,7 @@ def reshape(tensor, shape, name=None):
 
 
 
 
 def shape(input, name=None):
 def shape(input, name=None):
-    r"""Return the shape of input.
+    """Return the shape of input.
 
 
     Examples:
     Examples:
 
 
@@ -641,7 +636,7 @@ def split(
     axis=0,
     axis=0,
     name=None,
     name=None,
 ):
 ):
-    r"""Split input into chunks along the given axis.
+    """Split input into chunks along the given axis.
 
 
     Either number or size of splits will be accepted:
     Either number or size of splits will be accepted:
 
 
@@ -775,7 +770,7 @@ def zeros(shape, dtype='float32', name=None):
         A optional name for the operation.
         A optional name for the operation.
 
 
     """
     """
-    return init_ops.fill(shape, value=0., dtype=str(dtype), name=name)
+    return init_ops.fill(shape, value=0., dtype=dtype, name=name)
 
 
 
 
 def zeros_like(input, dtype='float32', name=None):
 def zeros_like(input, dtype='float32', name=None):
@@ -800,4 +795,4 @@ def zeros_like(input, dtype='float32', name=None):
         A optional name for the operation.
         A optional name for the operation.
 
 
     """
     """
-    return init_ops.zeros_like(input, dtype=str(dtype), name=name)
+    return init_ops.zeros_like(input, dtype=dtype, name=name)

tensorflow/core/ops/init_ops.py

@@ -79,7 +79,7 @@ class Constant(Initializer):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        dtype = str(self.dtype) if dtype is None else str(dtype)
+        dtype = str(self.dtype) if dtype is None else dtype
         return init_ops.fill(shape, value=self.value, dtype=dtype)
         return init_ops.fill(shape, value=self.value, dtype=dtype)
 
 
 
 
@@ -125,7 +125,7 @@ class RandomNormal(Initializer):
             shape=shape,
             shape=shape,
             mean=self.mean,
             mean=self.mean,
             std=self.stddev,
             std=self.stddev,
-            dtype=str(self.dtype) if dtype is None else str(dtype),
+            dtype=str(self.dtype) if dtype is None else dtype,
         )
         )
 
 
 
 
@@ -167,12 +167,12 @@ class RandomUniform(Initializer):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        dtype = str(self.dtype) if dtype is None else str(dtype)
+        dtype = str(self.dtype) if dtype is None else dtype
         return init_ops.random_uniform(
         return init_ops.random_uniform(
             shape=shape,
             shape=shape,
             low=self.minval,
             low=self.minval,
             high=self.maxval,
             high=self.maxval,
-            dtype=str(self.dtype) if dtype is None else str(dtype),
+            dtype=str(self.dtype) if dtype is None else dtype,
         )
         )
 
 
 
 
@@ -218,7 +218,7 @@ class TruncatedNormal(Initializer):
             shape=shape,
             shape=shape,
             mean=self.mean,
             mean=self.mean,
             std=self.stddev,
             std=self.stddev,
-            dtype=str(self.dtype) if dtype is None else str(dtype),
+            dtype=str(self.dtype) if dtype is None else dtype,
         )
         )
 
 
 
 
@@ -280,14 +280,14 @@ class VarianceScaling(Initializer):
                 shape=shape,
                 shape=shape,
                 mode=self.mode,
                 mode=self.mode,
                 scale=self.scale * 2.0,
                 scale=self.scale * 2.0,
-                dtype=str(self.dtype) if dtype is None else str(dtype)
+                dtype=str(self.dtype) if dtype is None else dtype
             )
             )
         else:
         else:
             return init_ops.glorot_uniform(
             return init_ops.glorot_uniform(
                 shape=shape,
                 shape=shape,
                 mode=self.mode,
                 mode=self.mode,
                 scale=self.scale * 3.0,
                 scale=self.scale * 3.0,
-                dtype=str(self.dtype) if dtype is None else str(dtype)
+                dtype=str(self.dtype) if dtype is None else dtype
             )
             )
 
 
 
 
@@ -374,8 +374,8 @@ class Ones(Initializer):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        dtype = str(self.dtype) if dtype is None else str(dtype)
-        return init_ops.fill(shape, value=1, dtype=str(dtype))
+        dtype = str(self.dtype) if dtype is None else dtype
+        return init_ops.fill(shape, value=1, dtype=dtype)
 
 
 
 
 class Zeros(Initializer):
 class Zeros(Initializer):
@@ -412,7 +412,7 @@ class Zeros(Initializer):
             The output tensor.
             The output tensor.
 
 
         """
         """
-        dtype = str(self.dtype) if dtype is None else str(dtype)
+        dtype = str(self.dtype) if dtype is None else dtype
         return init_ops.fill(shape, value=0, dtype=dtype)
         return init_ops.fill(shape, value=0, dtype=dtype)
 
 
 
 

tensorflow/core/ops/linalg_ops.py

@@ -48,4 +48,4 @@ def eye(num_rows, num_columns=None, dtype='float32', name=None):
         The output tensor.
         The output tensor.
 
 
     """
     """
-    return init_ops.eye(num_rows, num_columns, dtype=str(dtype), name=name)
+    return init_ops.eye(num_rows, num_columns, dtype=dtype, name=name)

tensorflow/core/ops/math_ops.py

@@ -209,7 +209,7 @@ def cast(x, dtype, name=None):
         The output tensor.
         The output tensor.
 
 
     """
     """
-    return array_ops.cast(x, dtype=str(dtype), name=name)
+    return array_ops.cast(x, dtype=dtype, name=name)
 
 
 
 
 def ceil(x, name=None):
 def ceil(x, name=None):
@@ -890,7 +890,7 @@ def range(start, limit=None, delta=1, dtype='int64', name=None):
         start=start,
         start=start,
         stop=limit,
         stop=limit,
         step=delta,
         step=delta,
-        dtype=str(dtype),
+        dtype=dtype,
         name=name,
         name=name,
     )
     )
 
 

tensorflow/core/ops/nn_ops.py

@@ -56,12 +56,11 @@ def avg_pool(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    num_total_dims = input.get_shape().ndims
-    if num_total_dims is None:
+    if input.shape is not None:
+        num_total_dims = len(input.shape)
+    else:
         num_total_dims = len(ksize)
         num_total_dims = len(ksize)
     num_spatial_dims = num_total_dims - 2
     num_spatial_dims = num_total_dims - 2
-
-    # Make default parameters
     data_format = data_format if data_format else 'NHWC'
     data_format = data_format if data_format else 'NHWC'
     start_axis = 2 if data_format.startswith('NC') else 1
     start_axis = 2 if data_format.startswith('NC') else 1
     normalize_spatial_args = \
     normalize_spatial_args = \
@@ -74,7 +73,6 @@ def avg_pool(
     ksize = normalize_spatial_args('ksize', ksize)
     ksize = normalize_spatial_args('ksize', ksize)
     strides = normalize_spatial_args('strides', strides)
     strides = normalize_spatial_args('strides', strides)
     padding, pads = normalize_spatial_args('padding', padding)
     padding, pads = normalize_spatial_args('padding', padding)
-
     return getattr(vision_ops, 'pool{}d'.format(num_spatial_dims))(
     return getattr(vision_ops, 'pool{}d'.format(num_spatial_dims))(
         [input],
         [input],
         kernel_shape=ksize.shape[start_axis:start_axis + num_spatial_dims],
         kernel_shape=ksize.shape[start_axis:start_axis + num_spatial_dims],
@@ -173,12 +171,11 @@ def convolution(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    num_total_dims = filters.get_shape().ndims
-    if num_total_dims is None:
-        raise ValueError('Rank of `filters` must be determined.')
+    if filters.shape is not None:
+        num_total_dims = len(filters.shape)
+    else:
+        raise ValueError('Rank of <filters> must be determined.')
     num_spatial_dims = num_total_dims - 2
     num_spatial_dims = num_total_dims - 2
-
-    # Make default parameters
     data_format = data_format if data_format else 'NHWC'
     data_format = data_format if data_format else 'NHWC'
     start_axis = 2 if data_format.startswith('NC') else 1
     start_axis = 2 if data_format.startswith('NC') else 1
     normalize_spatial_args = \
     normalize_spatial_args = \
@@ -191,7 +188,6 @@ def convolution(
     strides = normalize_spatial_args('strides', strides)
     strides = normalize_spatial_args('strides', strides)
     dilations = normalize_spatial_args('dilations', dilations)
     dilations = normalize_spatial_args('dilations', dilations)
     padding, pads = normalize_spatial_args('padding', padding)
     padding, pads = normalize_spatial_args('padding', padding)
-
     return getattr(vision_ops, '{}{}d'.format(
     return getattr(vision_ops, '{}{}d'.format(
         kwargs.get('conv_type', 'conv'), num_spatial_dims))(
         kwargs.get('conv_type', 'conv'), num_spatial_dims))(
             [input, filters],
             [input, filters],
@@ -241,14 +237,13 @@ def conv_transpose(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    num_total_dims = filters.get_shape().ndims
-    if num_total_dims is None:
-        num_total_dims = input.get_shape().ndims
-    if num_total_dims is None:
-        raise ValueError("rank of input or filters must be known.")
+    if filters.shape is not None:
+        num_total_dims = len(filters.shape)
+    elif input.shape is not None:
+        num_total_dims = len(input.shape)
+    else:
+        raise ValueError('Rank of <input> or <filters> must be known.')
     num_spatial_dims = num_total_dims - 2
     num_spatial_dims = num_total_dims - 2
-
-    # Make default parameters
     data_format = data_format if data_format else 'NHWC'
     data_format = data_format if data_format else 'NHWC'
     start_axis = 2 if data_format.startswith('NC') else 1
     start_axis = 2 if data_format.startswith('NC') else 1
     normalize_spatial_args = \
     normalize_spatial_args = \
@@ -264,7 +259,6 @@ def conv_transpose(
     if padding == 'SAME' and output_shape is None:
     if padding == 'SAME' and output_shape is None:
         raise ValueError('Excepted <output_shape> for same padding.')
         raise ValueError('Excepted <output_shape> for same padding.')
     output_shape = normalize_spatial_args('output_shape', output_shape)
     output_shape = normalize_spatial_args('output_shape', output_shape)
-
     return getattr(vision_ops, 'conv{}d_transpose'.format(num_spatial_dims))(
     return getattr(vision_ops, 'conv{}d_transpose'.format(num_spatial_dims))(
         [input, filters],
         [input, filters],
         kernel_shape=filters.shape[2:],
         kernel_shape=filters.shape[2:],
@@ -608,12 +602,11 @@ def max_pool(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    num_total_dims = input.get_shape().ndims
-    if num_total_dims is None:
+    if input.shape is not None:
+        num_total_dims = len(input.shape)
+    else:
         num_total_dims = len(ksize)
         num_total_dims = len(ksize)
     num_spatial_dims = num_total_dims - 2
     num_spatial_dims = num_total_dims - 2
-
-    # Make default parameters
     data_format = data_format if data_format else 'NHWC'
     data_format = data_format if data_format else 'NHWC'
     start_axis = 2 if data_format.startswith('NC') else 1
     start_axis = 2 if data_format.startswith('NC') else 1
     normalize_spatial_args = \
     normalize_spatial_args = \
@@ -626,7 +619,6 @@ def max_pool(
     ksize = normalize_spatial_args('ksize', ksize)
     ksize = normalize_spatial_args('ksize', ksize)
     strides = normalize_spatial_args('strides', strides)
     strides = normalize_spatial_args('strides', strides)
     padding, pads = normalize_spatial_args('padding', padding)
     padding, pads = normalize_spatial_args('padding', padding)
-
     return getattr(vision_ops, 'pool{}d'.format(num_spatial_dims))(
     return getattr(vision_ops, 'pool{}d'.format(num_spatial_dims))(
         [input],
         [input],
         kernel_shape=ksize[start_axis:start_axis + num_spatial_dims],
         kernel_shape=ksize[start_axis:start_axis + num_spatial_dims],

tensorflow/core/ops/random_ops.py

@@ -50,7 +50,7 @@ def random_normal(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    _, dtype, init_fn = seed, str(dtype), init_ops.random_normal
+    _, dtype, init_fn = seed, dtype, init_ops.random_normal
     return init_fn(shape, mean, stddev, dtype=dtype, name=name)
     return init_fn(shape, mean, stddev, dtype=dtype, name=name)
 
 
 
 
@@ -87,7 +87,7 @@ def random_uniform(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    _, dtype, init_fn = seed, str(dtype), init_ops.random_uniform
+    _, dtype, init_fn = seed, dtype, init_ops.random_uniform
     return init_fn(shape, minval, maxval, dtype=dtype, name=name)
     return init_fn(shape, minval, maxval, dtype=dtype, name=name)
 
 
 
 
@@ -125,5 +125,5 @@ def truncated_normal(
         The output tensor.
         The output tensor.
 
 
     """
     """
-    _, dtype, init_fn = seed, str(dtype), init_ops.truncated_normal
+    _, dtype, init_fn = seed, dtype, init_ops.truncated_normal
     return init_fn(shape, mean, stddev, dtype=dtype, name=name)
     return init_fn(shape, mean, stddev, dtype=dtype, name=name)

tensorflow/core/ops/variables.py

@@ -43,27 +43,21 @@ class Variable(VariableMetaclass, EagerTensor):
     ):
     ):
         """Create a ``Variable``."""
         """Create a ``Variable``."""
         super(Variable, self).__init__(trainable=trainable)
         super(Variable, self).__init__(trainable=trainable)
-
         name = name if name else 'Variable'
         name = name if name else 'Variable'
         self._name = context.get_name_scope() + name + ':0'
         self._name = context.get_name_scope() + name + ':0'
-
         # Determine th value.
         # Determine th value.
         if isinstance(initial_value, EagerTensor):
         if isinstance(initial_value, EagerTensor):
             initial_value = initial_value.numpy()
             initial_value = initial_value.numpy()
         elif isinstance(initial_value, Tensor):
         elif isinstance(initial_value, Tensor):
             initial_value = initial_value.get_value()
             initial_value = initial_value.get_value()
-
         # Determine the data type.
         # Determine the data type.
         if not isinstance(initial_value, numpy.ndarray):
         if not isinstance(initial_value, numpy.ndarray):
-            initial_value = numpy.array(
-                initial_value, str(dtype) if dtype else dtype)
+            initial_value = numpy.array(initial_value, dtype if dtype else dtype)
         elif dtype is not None:
         elif dtype is not None:
-            initial_value = initial_value.astype(str(dtype))
-
+            initial_value = initial_value.astype(dtype)
         # Determine the tensor shape.
         # Determine the tensor shape.
         if shape is not None:
         if shape is not None:
             initial_value = initial_value.reshape(shape)
             initial_value = initial_value.reshape(shape)
-
         self._from_numpy(initial_value, copy=False)
         self._from_numpy(initial_value, copy=False)
 
 
     @property
     @property
@@ -96,7 +90,6 @@ def get_default_initializer(name, shape=None, dtype=dtypes.float32):
     # Defaults: float32.
     # Defaults: float32.
     if dtype is None:
     if dtype is None:
         dtype = dtypes.float32
         dtype = dtypes.float32
-
     # Xavier for float16, float32, float64.
     # Xavier for float16, float32, float64.
     if dtype.is_floating:
     if dtype.is_floating:
         initializer = init_ops.glorot_uniform_initializer()
         initializer = init_ops.glorot_uniform_initializer()

test/dragon/core/test_framework.py

@@ -67,20 +67,20 @@ class TestTensor(unittest.TestCase):
         self.assertEqual(dragon.Tensor().ndim, 0)
         self.assertEqual(dragon.Tensor().ndim, 0)
         self.assertEqual(dragon.Tensor(shape=(2,)).ndim, 1)
         self.assertEqual(dragon.Tensor(shape=(2,)).ndim, 1)
         self.assertEqual(dragon.Tensor().shape, None)
         self.assertEqual(dragon.Tensor().shape, None)
-        self.assertEqual(dragon.Tensor(shape=(2,)).shape, [2])
+        self.assertEqual(dragon.Tensor(shape=(2,)).shape, (2,))
         self.assertEqual(dragon.Tensor().size, 0)
         self.assertEqual(dragon.Tensor().size, 0)
         self.assertEqual(dragon.Tensor(shape=(2, None)).size, math.inf)
         self.assertEqual(dragon.Tensor(shape=(2, None)).size, math.inf)
         self.assertEqual(dragon.Tensor(shape=(2,)).size, 2)
         self.assertEqual(dragon.Tensor(shape=(2,)).size, 2)
         self.assertEqual(dragon.Tensor().dtype, None)
         self.assertEqual(dragon.Tensor().dtype, None)
         self.assertEqual(dragon.Tensor(dtype='float32').dtype, 'float32')
         self.assertEqual(dragon.Tensor(dtype='float32').dtype, 'float32')
         self.assertEqual(dragon.EagerTensor(shape=(2,)).ndim, 1)
         self.assertEqual(dragon.EagerTensor(shape=(2,)).ndim, 1)
-        self.assertEqual(dragon.EagerTensor(shape=(2,)).shape, [2])
+        self.assertEqual(dragon.EagerTensor(shape=(2,)).shape, (2,))
         self.assertEqual(dragon.EagerTensor(shape=(2,)).size, 2)
         self.assertEqual(dragon.EagerTensor(shape=(2,)).size, 2)
         self.assertEqual(dragon.EagerTensor(shape=(2,), dtype='float32').dtype, 'float32')
         self.assertEqual(dragon.EagerTensor(shape=(2,), dtype='float32').dtype, 'float32')
         self.assertEqual(dragon.EagerTensor().device, dragon.EagerTensor().device)
         self.assertEqual(dragon.EagerTensor().device, dragon.EagerTensor().device)
         self.assertNotEqual(a.__hash__(), b.__hash__())
         self.assertNotEqual(a.__hash__(), b.__hash__())
         self.assertNotEqual(a.__repr__(), b.__repr__())
         self.assertNotEqual(a.__repr__(), b.__repr__())
-        self.assertNotEqual(b.__repr__(), dragon.EagerTensor([2]).__repr__())
+        self.assertNotEqual(b.__repr__(), dragon.EagerTensor((2,)).__repr__())
         self.assertEqual(int(a.constant().set_value(1)), 1)
         self.assertEqual(int(a.constant().set_value(1)), 1)
         self.assertEqual(float(dragon.Tensor.convert_to(1)), 1.)
         self.assertEqual(float(dragon.Tensor.convert_to(1)), 1.)
         self.assertEqual(int(b.set_value(1)), 1)
         self.assertEqual(int(b.set_value(1)), 1)
@@ -117,7 +117,7 @@ class TestTensor(unittest.TestCase):
             x = dragon.EagerTensor(data, copy=True)
             x = dragon.EagerTensor(data, copy=True)
         x_to_dlpack = dragon.dlpack.to_dlpack(x)
         x_to_dlpack = dragon.dlpack.to_dlpack(x)
         x_from_dlpack = dragon.dlpack.from_dlpack(x_to_dlpack)
         x_from_dlpack = dragon.dlpack.from_dlpack(x_to_dlpack)
-        self.assertEqual(x_from_dlpack.shape, list(data.shape))
+        self.assertEqual(x_from_dlpack.shape, data.shape)
         self.assertEqual(x_from_dlpack.dtype, str(data.dtype))
         self.assertEqual(x_from_dlpack.dtype, str(data.dtype))
         self.assertLessEqual(np.abs(x_from_dlpack.numpy() - data).max(), 1e-5)
         self.assertLessEqual(np.abs(x_from_dlpack.numpy() - data).max(), 1e-5)
 
 
@@ -130,7 +130,7 @@ class TestTensor(unittest.TestCase):
         x_from_dlpack = dragon.dlpack.from_dlpack(x_to_dlpack)
         x_from_dlpack = dragon.dlpack.from_dlpack(x_to_dlpack)
         self.assertEqual(x_from_dlpack.device.type, 'cuda')
         self.assertEqual(x_from_dlpack.device.type, 'cuda')
         self.assertEqual(x_from_dlpack.device.index, 0)
         self.assertEqual(x_from_dlpack.device.index, 0)
-        self.assertEqual(x_from_dlpack.shape, list(data.shape))
+        self.assertEqual(x_from_dlpack.shape, data.shape)
         self.assertEqual(x_from_dlpack.dtype, str(data.dtype))
         self.assertEqual(x_from_dlpack.dtype, str(data.dtype))
         self.assertLessEqual(np.abs(x_from_dlpack.numpy() - data).max(), 1e-5)
         self.assertLessEqual(np.abs(x_from_dlpack.numpy() - data).max(), 1e-5)
 
 
@@ -150,7 +150,7 @@ class TestWorkspace(unittest.TestCase):
         w = dragon.Workspace()
         w = dragon.Workspace()
         with w.as_default():
         with w.as_default():
             v1, v2 = dragon.EagerTensor(1), np.array(2)
             v1, v2 = dragon.EagerTensor(1), np.array(2)
-            x = dragon.Tensor('test_feed_tensor/x')
+            x = dragon.Tensor(name='test_feed_tensor/x')
             w.feed_tensor(x, v1)
             w.feed_tensor(x, v1)
             self.assertEqual(int(x), 1)
             self.assertEqual(int(x), 1)
             w.feed_tensor(x, v2)
             w.feed_tensor(x, v2)
@@ -159,7 +159,7 @@ class TestWorkspace(unittest.TestCase):
     def test_merge_form(self):
     def test_merge_form(self):
         w1, w2 = dragon.Workspace(), dragon.Workspace()
         w1, w2 = dragon.Workspace(), dragon.Workspace()
         with w1.as_default():
         with w1.as_default():
-            x = dragon.Tensor('test_merge_from/x').set_value(0)
+            x = dragon.Tensor(name='test_merge_from/x').set_value(0)
         w2.merge_from(w1)
         w2.merge_from(w1)
         with w2.as_default():
         with w2.as_default():
             self.assertEqual(int(x), 0)
             self.assertEqual(int(x), 0)

test/dragon/core/test_ops.py

@@ -67,7 +67,7 @@ class OpTestCase(unittest.TestCase):
                     dtype = symbols[i][1].dtype
                     dtype = symbols[i][1].dtype
                     shape = symbols[i][1].shape
                     shape = symbols[i][1].shape
                     super(OpTestCase, self).assertEqual(dtype, str(values[i].dtype))
                     super(OpTestCase, self).assertEqual(dtype, str(values[i].dtype))
-                    super(OpTestCase, self).assertEqual(shape, list(shape))
+                    super(OpTestCase, self).assertEqual(shape, values[i].shape)
                 inputs[symbols[i][0]] = values[i]
                 inputs[symbols[i][0]] = values[i]
         first = inputs[:num_first] if num_first > 1 else inputs[0]
         first = inputs[:num_first] if num_first > 1 else inputs[0]
         second = inputs[num_first:len(inputs)] if num_second > 1 else inputs[num_first]
         second = inputs[num_first:len(inputs)] if num_second > 1 else inputs[num_first]
@@ -239,7 +239,7 @@ class TestActivationOps(OpTestCase):
                 for x_shape, w_shape, data_format in entries:
                 for x_shape, w_shape, data_format in entries:
                     data1 = uniform(x_shape)
                     data1 = uniform(x_shape)
                     data2 = np.ones(w_shape, 'float32') * 0.25
                     data2 = np.ones(w_shape, 'float32') * 0.25
-                    x, w = new_tensor(data1), new_tensor(data2)
+                    x, w = new_tensor(data1), new_tensor(data2.flatten())
                     with dragon.GradientTape() as tape:
                     with dragon.GradientTape() as tape:
                         tape.watch([x, w])
                         tape.watch([x, w])
                         y = dragon.nn.prelu([x, w], data_format=data_format)
                         y = dragon.nn.prelu([x, w], data_format=data_format)
@@ -632,7 +632,7 @@ class TestArrayOps(OpTestCase):
                     tape.watch(x)
                     tape.watch(x)
                     y = dragon.masked_select([x, x > 2])
                     y = dragon.masked_select([x, x > 2])
                 dx = tape.gradient(y, [x], output_gradients=[y])[0]
                 dx = tape.gradient(y, [x], output_gradients=[y])[0]
-                self.assertEqual([y, dx], [data[data > 2], grad])
+                self.assertEqual([y, dx], [data[data > 2], grad], test_symbols=False)
 
 
     @unittest.skipIf(not TEST_CUDA, 'CUDA unavailable')
     @unittest.skipIf(not TEST_CUDA, 'CUDA unavailable')
     def test_masked_select_cuda(self):
     def test_masked_select_cuda(self):
@@ -645,7 +645,8 @@ class TestArrayOps(OpTestCase):
                 data = arange((2, 3))
                 data = arange((2, 3))
                 x = new_tensor(data)
                 x = new_tensor(data)
                 y = dragon.nonzero(x > 2)
                 y = dragon.nonzero(x > 2)
-                self.assertEqual(y, np.stack(np.nonzero(data > 2), axis=1))
+                self.assertEqual(
+                    y, np.stack(np.nonzero(data > 2), axis=1), test_symbols=False)
 
 
     @unittest.skipIf(not TEST_CUDA, 'CUDA unavailable')
     @unittest.skipIf(not TEST_CUDA, 'CUDA unavailable')
     def test_non_zero_cuda(self):
     def test_non_zero_cuda(self):
@@ -699,7 +700,7 @@ class TestArrayOps(OpTestCase):
                         tape.watch(x)
                         tape.watch(x)
                         y = dragon.repeat(x, axis, repeats)
                         y = dragon.repeat(x, axis, repeats)
                     grad = arange(y.shape)
                     grad = arange(y.shape)
-                    grad_shape = y.shape[:-1] + [y.shape[-1] // 2, 2]
+                    grad_shape = y.shape[:-1] + (y.shape[-1] // 2, 2)
                     dy = new_tensor(grad)
                     dy = new_tensor(grad)
                     dx = tape.gradient(y, [x], output_gradients=[dy])[0]
                     dx = tape.gradient(y, [x], output_gradients=[dy])[0]
                     self.assertEqual(
                     self.assertEqual(
@@ -2271,8 +2272,8 @@ class TestNormalizationOps(OpTestCase):
                     data4, data5 = arange(w_shape) * .1, arange(w_shape, 1) * .1
                     data4, data5 = arange(w_shape) * .1, arange(w_shape, 1) * .1
                     data6 = uniform(x_shape)
                     data6 = uniform(x_shape)
                     x = new_tensor(data1)
                     x = new_tensor(data1)
-                    w, b = new_tensor(data2), new_tensor(data3)
-                    rm, rv = new_tensor(data4), new_tensor(data5)
+                    w, b = new_tensor(data2.flatten()), new_tensor(data3.flatten())
+                    rm, rv = new_tensor(data4.flatten()), new_tensor(data5.flatten())
                     with dragon.GradientTape() as tape:
                     with dragon.GradientTape() as tape:
                         tape.watch([x, w, b])
                         tape.watch([x, w, b])
                         y = dragon.nn.batch_norm(
                         y = dragon.nn.batch_norm(
@@ -2330,7 +2331,7 @@ class TestNormalizationOps(OpTestCase):
                     data2, data3 = arange(w_shape, 1) * .1, arange(w_shape) * .1
                     data2, data3 = arange(w_shape, 1) * .1, arange(w_shape) * .1
                     data6 = arange(x_shape) * .1
                     data6 = arange(x_shape) * .1
                     x = new_tensor(data1)
                     x = new_tensor(data1)
-                    w, b = new_tensor(data2), new_tensor(data3)
+                    w, b = new_tensor(data2.flatten()), new_tensor(data3.flatten())
                     with dragon.GradientTape() as tape:
                     with dragon.GradientTape() as tape:
                         tape.watch([x, w, b])
                         tape.watch([x, w, b])
                         y = dragon.nn.group_norm(
                         y = dragon.nn.group_norm(
@@ -2374,7 +2375,7 @@ class TestNormalizationOps(OpTestCase):
                     data2, data3 = arange(w_shape, 1) * .1, arange(w_shape) * .1
                     data2, data3 = arange(w_shape, 1) * .1, arange(w_shape) * .1
                     data6 = arange(x_shape) * 10.
                     data6 = arange(x_shape) * 10.
                     x = new_tensor(data1)
                     x = new_tensor(data1)
-                    w, b = new_tensor(data2), new_tensor(data3)
+                    w, b = new_tensor(data2.flatten()), new_tensor(data3.flatten())
                     with dragon.GradientTape() as tape:
                     with dragon.GradientTape() as tape:
                         tape.watch([x, w, b])
                         tape.watch([x, w, b])
                         y = dragon.nn.instance_norm([x, w, b], axis=axis, eps=eps)
                         y = dragon.nn.instance_norm([x, w, b], axis=axis, eps=eps)
@@ -2417,7 +2418,7 @@ class TestNormalizationOps(OpTestCase):
                     data2, data3 = arange(w_shape, 1) * .1, arange(w_shape) * .1
                     data2, data3 = arange(w_shape, 1) * .1, arange(w_shape) * .1
                     data6 = arange(x_shape) * 10.
                     data6 = arange(x_shape) * 10.
                     x = new_tensor(data1)
                     x = new_tensor(data1)
-                    w, b = new_tensor(data2), new_tensor(data3)
+                    w, b = new_tensor(data2.flatten()), new_tensor(data3.flatten())
                     with dragon.GradientTape() as tape:
                     with dragon.GradientTape() as tape:
                         tape.watch([x, w, b])
                         tape.watch([x, w, b])
                         y = dragon.nn.layer_norm([x, w, b], axis=axis, eps=eps)
                         y = dragon.nn.layer_norm([x, w, b], axis=axis, eps=eps)
@@ -2587,7 +2588,7 @@ class TestTensorOps(OpTestCase):
                 grad[data > 2] = 1
                 grad[data > 2] = 1
                 grad *= data
                 grad *= data
                 x = new_tensor(data)
                 x = new_tensor(data)
-                self.assertEqual(x[x > 2], data[data > 2])
+                self.assertEqual(x[x > 2], data[data > 2], test_symbols=False)
                 entries = [0,
                 entries = [0,
                            slice(None, None, None),
                            slice(None, None, None),
                            slice(0, None, None),
                            slice(0, None, None),
@@ -2885,13 +2886,13 @@ class TestVisionOps(OpTestCase):
             with execution_context().mode(execution):
             with execution_context().mode(execution):
                 for x_shape, b_shape, data_format in entries:
                 for x_shape, b_shape, data_format in entries:
                     data1, data2 = arange(x_shape), arange(b_shape)
                     data1, data2 = arange(x_shape), arange(b_shape)
-                    x, w = new_tensor(data1), new_tensor(data2)
+                    x, b = new_tensor(data1), new_tensor(data2.flatten())
                     with dragon.GradientTape() as tape:
                     with dragon.GradientTape() as tape:
-                        tape.watch([x, w])
-                        y = dragon.nn.bias_add([x, w], data_format)
-                    dx, dw = tape.gradient(y, [x, w], output_gradients=[x])
+                        tape.watch([x, b])
+                        y = dragon.nn.bias_add([x, b], data_format)
+                    dx, db = tape.gradient(y, [x, b], output_gradients=[x])
                     self.assertEqual(
                     self.assertEqual(
-                        [y, dx, dw],
+                        [y, dx, db],
                         [data1 + data2, data1, reduce_like(data1, data2).flatten()])
                         [data1 + data2, data1, reduce_like(data1, data2).flatten()])
 
 
     @unittest.skipIf(not TEST_CUDA, 'CUDA unavailable')
     @unittest.skipIf(not TEST_CUDA, 'CUDA unavailable')
@@ -3422,7 +3423,7 @@ def new_tensor(data):
     """Create a new tensor for current execution."""
     """Create a new tensor for current execution."""
     if execution_context().executing_eagerly():
     if execution_context().executing_eagerly():
         return dragon.EagerTensor(data, copy=True)
         return dragon.EagerTensor(data, copy=True)
-    return dragon.Tensor(None, data.shape, str(data.dtype)).set_value(data)
+    return dragon.Tensor(data.shape, str(data.dtype)).set_value(data)
 
 
 
 
 def process_indices(item):
 def process_indices(item):

torch/__init__.py

@@ -24,6 +24,7 @@ from dragon.vm.torch import vision
 # Classes
 # Classes
 from dragon.vm.torch.autograd import Variable
 from dragon.vm.torch.autograd import Variable
 from dragon.vm.torch.cpp import device
 from dragon.vm.torch.cpp import device
+from dragon.vm.torch.cpp import dtype
 from dragon.vm.torch.cpp import Size
 from dragon.vm.torch.cpp import Size
 from dragon.vm.torch.tensor import ByteTensor
 from dragon.vm.torch.tensor import ByteTensor
 from dragon.vm.torch.tensor import CharTensor
 from dragon.vm.torch.tensor import CharTensor
@@ -116,3 +117,21 @@ from dragon.vm.torch.serialization import load
 from dragon.vm.torch.serialization import save
 from dragon.vm.torch.serialization import save
 from dragon.vm.torch.tensor import empty
 from dragon.vm.torch.tensor import empty
 from dragon.vm.torch.tensor import tensor
 from dragon.vm.torch.tensor import tensor
+
+# Aliases
+bool = dtype('bool')
+int8 = dtype('int8')
+uint8 = dtype('uint8')
+int16 = short = dtype('int16')
+int32 = int = dtype('int32')
+int64 = long = dtype('int64')
+qint8 = dtype('qint8')
+quint8 = dtype('quint8')
+qint32 = dtype('qint32')
+bfloat16 = dtype('bfloat16')
+float16 = half = dtype('float16')
+float32 = float = dtype('float32')
+float64 = double = dtype('float64')
+complex32 = dtype('complex32')
+complex64 = dtype('complex64')
+complex128 = dtype('complex128')

torch/cpp.py

@@ -58,7 +58,7 @@ class Size(tuple):
 
 
 
 
 class device(object):
 class device(object):
-    """Represent the device where tensor will be allocated."""
+    """Represent the device spec."""
 
 
     def __init__(self, type='cpu', index=0):
     def __init__(self, type='cpu', index=0):
         """Create a ``device``.
         """Create a ``device``.
@@ -100,6 +100,57 @@ class device(object):
         return 'device(type={}, index={})'.format(self.type, self.index)
         return 'device(type={}, index={})'.format(self.type, self.index)
 
 
 
 
+class dtype(str):
+    """The basic data type.
+
+    Following data types are defined:
+
+    * ``torch.float16`` or ``torch.half``: 16-bit half-precision floating-point.
+
+    * ``torch.float32`` or ``torch.float``: 32-bit single-precision floating-point.
+
+    * ``torch.float64`` or ``torch.double``: 64-bit double-precision floating-point.
+
+    * ``torch.bfloat16``: 16-bit truncated floating-point.
+
+    * ``torch.complex32``: 32-bit single-precision complex.
+
+    * ``torch.complex64``: 64-bit single-precision complex.
+
+    * ``torch.complex128``: 128-bit double-precision complex.
+
+    * ``torch.int8``: 8-bit signed integer.
+
+    * ``torch.uint8``: 8-bit unsigned integer.
+
+    * ``torch.int16`` or ``torch.short``: 16-bit signed integer.
+
+    * ``torch.int32`` or ``torch.int``: 32-bit signed integer.
+
+    * ``torch.int64`` or ``torch.long``: 64-bit signed integer.
+
+    * ``torch.bool``: Boolean.
+
+    * ``torch.qint8``: Quantized 8-bit signed integer.
+
+    * ``torch.quint8``: Quantized 8-bit unsigned integer.
+
+    * ``torch.qint32``: Quantized 32-bit signed integer.
+
+    """
+
+    def __init__(self, s):
+        """Create a ``dtype``.
+
+        Parameters
+        ----------
+        s : str
+            The data type descriptor.
+
+        """
+        super(dtype, self).__init__()
+
+
 def from_numpy(array):
 def from_numpy(array):
     """Create a tensor from the given numpy array.
     """Create a tensor from the given numpy array.
 
 

torch/ops/tensorbind.py

@@ -636,7 +636,7 @@ def getitem(self, item):
 
 
     Parameters
     Parameters
     ----------
     ----------
-    item : Union[int, slice, dragon.vm.torch.Tensor]
+    item : Union[slice, int, dragon.vm.torch.Tensor]
         The index.
         The index.
 
 
     Returns
     Returns
@@ -1318,7 +1318,7 @@ def setitem(self, key, value):
 
 
     Parameters
     Parameters
     ----------
     ----------
-    key : Union[int, slice, dragon.vm.torch.Tensor]
+    key : Union[slice, int, dragon.vm.torch.Tensor]
         The index.
         The index.
     value : Union[dragon.vm.torch.Tensor, number]
     value : Union[dragon.vm.torch.Tensor, number]
         The value to set.
         The value to set.

torch/tensor.py

@@ -1940,7 +1940,7 @@ class Tensor(object):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        item : Union[int, slice, dragon.vm.torch.Tensor]
+        item : Union[slice, int, dragon.vm.torch.Tensor]
             The index.
             The index.
 
 
         Returns
         Returns
@@ -2198,7 +2198,7 @@ class Tensor(object):
 
 
         Parameters
         Parameters
         ----------
         ----------
-        key : Union[int, slice, dragon.vm.torch.Tensor]
+        key : Union[slice, int, dragon.vm.torch.Tensor]
             The index.
             The index.
         value : Union[dragon.vm.torch.Tensor, number]
         value : Union[dragon.vm.torch.Tensor, number]
             The value to set.
             The value to set.