[FRONTEND][TENSORFLOW] Use input shapes directly instead of 1-element lists (#2242)

nnvm/python/nnvm/frontend/tensorflow.py

@@ -120,7 +120,7 @@ def _pooling(name):
         attr['data_format'] = attr['data_format'].decode("utf-8")
         flip_layout = False
 
-        input_shape = attr['_input_shapes'][inputs[0]][0]
+        input_shape = attr['_input_shapes'][inputs[0]]
 
         if attr['data_format'] == 'NHWC':
             attr['kernel_shape'] = (attr['ksize'][1], attr['ksize'][2])
@@ -132,7 +132,7 @@ def _pooling(name):
             raise TypeError("Unsupported data_format type : {}".format(attr['data_format']))
 
         if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
-            tmp_shape = attr['_input_shapes'][inputs[0]][0]
+            tmp_shape = attr['_input_shapes'][inputs[0]]
             input_shape = [tmp_shape[ii] for ii in (0, 3, 1, 2)]
             inputs[0] = _sym.transpose(inputs[0], axes=(0, 3, 1, 2))
             attr['data_format'] = "NCHW"
@@ -185,13 +185,13 @@ def _conv(opname):
 
         # NCHW Layout require weights transpose
         if attr['data_format'] == 'NCHW':
-            tmp_shape = attr['_input_shapes'][inputs[1]][0]
+            tmp_shape = attr['_input_shapes'][inputs[1]]
             tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
             inputs[1] = _sym.transpose(inputs[1], axes=(3, 2, 0, 1))
-            attr['_input_shapes'][inputs[1]] = [tmp_shape]
+            attr['_input_shapes'][inputs[1]] = tmp_shape
 
-        input_shape = attr['_input_shapes'][inputs[0]][0]
-        weights_shape = attr['_input_shapes'][inputs[1]][0]
+        input_shape = attr['_input_shapes'][inputs[0]]
+        weights_shape = attr['_input_shapes'][inputs[1]]
 
         if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
             input_shape = [input_shape[ii] for ii in (0, 3, 1, 2)]
@@ -484,7 +484,7 @@ def _relu6():
 
 def _shape():
     def _impl(inputs, attr, params):
-        return np.array(attr['_input_shapes'][inputs[0]][0], dtype='int32')
+        return np.array(attr['_input_shapes'][inputs[0]], dtype='int32')
     return _impl
 
 def _fill():
@@ -565,7 +565,7 @@ def _stridedSlice():
         new_axis_mask = int(attr.get('new_axis_mask', 0))
         shrink_axis_mask = int(attr.get('shrink_axis_mask', 0))
         data_shape = attr['_input_shapes'][inputs[0]]
-        data_dim = len(data_shape[0])
+        data_dim = len(data_shape)
         stride_dim = len(stride)
 
         def _transform_mask(stride_dim, ellipsis_mask):
@@ -596,7 +596,7 @@ def _stridedSlice():
                                      + new_axes_after_ellipsis), data_dim)
                     for i in range(final_index, to_index):
                         m_begin[final_index] = 0
-                        m_end[final_index] = data_shape[0][final_index]
+                        m_end[final_index] = data_shape[final_index]
                         m_stride[final_index] = 1
                         fshape_indices.append(final_index)
                         final_index += 1
@@ -606,19 +606,19 @@ def _stridedSlice():
                     if final_index == len(m_begin):
                         break
                     if mask & begin_mask:
-                        m_begin[final_index] = data_shape[0][final_index] \
+                        m_begin[final_index] = data_shape[final_index] \
                                                      if stride[index] < 0 else 0
                     elif begin[index]:
                         m_begin[final_index] = begin[index]
                     if mask & end_mask:
                         m_end[final_index] = 0 if stride[index] < 0 \
-                                                 else data_shape[0][final_index]
+                                                 else data_shape[final_index]
                     elif end[index]:
                         m_end[final_index] = end[index]
                     m_stride[final_index] = stride[index]
                     if mask & shrink_axis_mask:
                         #Tensorflow make axis with shrink_axis_mask as dimension 1
-                        m_begin[final_index] = data_shape[0][final_index] + begin[index] \
+                        m_begin[final_index] = data_shape[final_index] + begin[index] \
                                                  if begin[index] < 0 else begin[index]
                         m_end[final_index] = begin[index] + 1
                         m_stride[final_index] = 1
@@ -684,8 +684,8 @@ def _LSTMBlockCell():
         forget_bias = attr.pop('forget_bias')
         input_shape = attr['_input_shapes'][inputs[0]]
         weight_shape = attr['_input_shapes'][inputs[3]]
-        batch_size, input_size = input_shape[0][0], input_shape[0][1]
-        num_hidden_layers = weight_shape[0][1]
+        batch_size, input_size = input_shape[0], input_shape[1]
+        num_hidden_layers = weight_shape[1]
         num_hidden = num_hidden_layers // 4
 
         in_data = _sym.reshape(in_data,
@@ -741,11 +741,10 @@ def _transpose():
 
 def _rank():
     def _impl(inputs, attr, params):
-        input_shapes = attr['_input_shapes'][inputs[0]]
-        assert len(inputs) == 1
+        input_shape = attr['_input_shapes'][inputs[0]]
 
         name = attr["_node_name"]
-        params[name] = tvm.nd.array([len(input_shapes[0])])
+        params[name] = tvm.nd.array([len(input_shape)])
         return _sym.Variable(name=name, shape=params[name].shape)
     return _impl
 
@@ -829,7 +828,7 @@ def _unpack():
     def _impl(inputs, attr, params):
         input_node = inputs[0]
         axis = attr['axis']
-        input_shape = attr['_input_shapes'][input_node][0]
+        input_shape = attr['_input_shapes'][input_node]
         axis_length = input_shape[axis]
         if axis_length < 0:
             raise TypeError("Unstack with unknown axis length")
@@ -1018,8 +1017,8 @@ class RecurrentNetworks(object):
                 """LSTM cell warapper to prepare the inputs"""
                 input_shape = attr['_input_shapes'][inputs[0]]
                 weight_shape = attr['_input_shapes'][inputs[3]]
-                batch_size = input_shape[0][0]
-                num_hidden = weight_shape[0][1] // 4
+                batch_size = input_shape[0]
+                num_hidden = weight_shape[1] // 4
 
                 if layer == 0:
                     #Create initial states placeholder in case of first layer
@@ -1240,7 +1239,7 @@ class GraphProto(object):
                             tensor_slot = 0
                             input_shape = self._output_shapes[node_name][0]
                         inputs.append(in_sym)
-                        input_shapes[in_sym] = [input_shape]
+                        input_shapes[in_sym] = input_shape
                         # This means the node is 1d in NNVM and 0d in TF.
                         # See `_expand_dims_0d_aware`.
                         if self._outputs_are_0d[node_name][tensor_slot] and input_shape: