[FRONTEND][TFLITE]Gather, StridedSlice op added

python/tvm/relay/frontend/tflite.py

@@ -112,6 +112,8 @@ def __init__(self, model, subgraph, exp_tab):
             'PRELU': self.convert_prelu,
             'TRANSPOSE_CONV': self.convert_transpose_conv,
             'SQUARED_DIFFERENCE': self.convert_squared_difference,
+            'GATHER': self.convert_gather,
+            'STRIDED_SLICE': self.convert_strided_slice,
         }
 
     def check_unsupported_ops(self):
@@ -747,6 +749,158 @@ def convert_squared_difference(self, op):
         out = _op.power(difference, relay.const(2, exp_type))
         return out
 
+    def convert_gather(self, op):
+        """Method to Convert TFLite Gather operator"""
+        # Check if the input tensor is quantized, call QNN op
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                'TFlite quantized gather operator is not supported yet.')
+        input_tensors = self.get_input_tensors(op)
+
+        try:
+            from tflite.BuiltinOptions import BuiltinOptions
+            from tflite.GatherOptions import GatherOptions
+            from tflite.TensorType import TensorType
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.GatherOptions
+        op_options = op.BuiltinOptions()
+        gather_options = GatherOptions()
+        gather_options.Init(op_options.Bytes, op_options.Pos)
+        axis = gather_options.Axis()
+
+        data = self.get_expr(input_tensors[0].tensor_idx)
+
+        indices = input_tensors[1]
+        indices_type = indices.tensor.Type()
+
+        assert indices_type in (TensorType.INT32, TensorType.INT64)
+        indices_type_str = self.get_tensor_type_str(indices_type)
+        indices = self.exp_tab.new_const(self.get_tensor_value(indices),
+                                         dtype=indices_type_str)
+        out = _op.take(data, indices, axis=axis)
+        return out
+
+    def convert_strided_slice(self, op):
+        """Method to Convert TFLite Strided Slice operator"""
+        # Check if the input tensor is quantized, call QNN op
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                'TFlite quantized strided slice operator is not supported yet.')
+        input_tensors = self.get_input_tensors(op)
+
+        try:
+            from tflite.BuiltinOptions import BuiltinOptions
+            from tflite.StridedSliceOptions import StridedSliceOptions
+        except ImportError:
+            raise ImportError("The tflite package must be installed")
+
+        data_expr = self.get_expr(input_tensors[0].tensor_idx)
+
+        begin = list(self.get_tensor_value(input_tensors[1]))
+        end = list(self.get_tensor_value(input_tensors[2]))
+        stride = list(self.get_tensor_value(input_tensors[3]))
+
+        assert op.BuiltinOptionsType() == BuiltinOptions.StridedSliceOptions
+        op_options = op.BuiltinOptions()
+        options = StridedSliceOptions()
+        options.Init(op_options.Bytes, op_options.Pos)
+        begin_mask = options.BeginMask()
+        end_mask = options.EndMask()
+        ellipsis_mask = options.EllipsisMask()
+        new_axis_mask = options.NewAxisMask()
+        shrink_axis_mask = options.ShrinkAxisMask()
+
+        data_shape = list(input_tensors[0].tensor.ShapeAsNumpy())
+
+        data_dim = len(data_shape)
+        stride_dim = len(list(input_tensors[3].tensor.ShapeAsNumpy()))
+
+        def _transform_mask(stride_dim, ellipsis_mask):
+            """Handle mask inputs to create new begin, end, stride and output shape"""
+            m_begin = [0] * data_dim
+            m_end = [0] * data_dim
+            m_stride = [0] * data_dim
+            fshape_indices = []
+            #Count new axis after ellipsis_mask, consider while applying ellipsis_mask.
+            ellipsis_seen = False
+            new_axes_after_ellipsis = 0
+            for i in range(stride_dim):
+                mask = 1 << i
+                if ellipsis_seen and (mask & new_axis_mask) != 0:
+                    new_axes_after_ellipsis += 1
+                if (mask & ellipsis_mask) != 0:
+                    ellipsis_seen = True
+            if not ellipsis_seen:
+                #Used later for extending the stride attributes in the below loop.
+                ellipsis_mask |= (1 << stride_dim)
+                stride_dim += 1
+            final_index = 0
+            for index in range(stride_dim):
+                mask = 1 << index
+                if mask & ellipsis_mask:
+                    #Identify the end index for applying ellipsis_mask
+                    to_index = min(((data_dim - (stride_dim-index)) + 1 \
+                                     + 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[final_index]
+                        m_stride[final_index] = 1
+                        fshape_indices.append(final_index)
+                        final_index += 1
+                elif mask &new_axis_mask:
+                    fshape_indices.append(-1)
+                elif not mask & new_axis_mask:
+                    if final_index == len(m_begin):
+                        break
+                    if mask & begin_mask:
+                        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[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[final_index] + begin[index] \
+                                                 if begin[index] < 0 else begin[index]
+                        m_end[final_index] = begin[index] + 1
+                        m_stride[final_index] = 1
+                        fshape_indices.append(-2)
+                    else:
+                        fshape_indices.append(final_index)
+
+                    final_index += 1
+            return m_begin, m_end, m_stride, fshape_indices
+
+        fshape_indices = None
+        if begin_mask or end_mask or ellipsis_mask or new_axis_mask or shrink_axis_mask:
+            begin, end, stride, fshape_indices = _transform_mask(stride_dim, ellipsis_mask)
+
+        out = _op.strided_slice(data_expr, begin=begin, end=end, strides=stride)
+        out_shape = _infer_shape(out)
+        if not fshape_indices:
+            fshape_indices = range(len(out_shape))
+
+        #Create final output shape.
+        final_output = []
+        for gather_index in fshape_indices:
+            if gather_index == -1:
+                final_output.append(1)
+            elif gather_index == -2:
+                pass
+            else:
+                final_output.append(out_shape[gather_index])
+
+        if not final_output:
+            return out
+        return _op.reshape(out, newshape=tuple(final_output))
+
     def convert_zeros_like(self, op):
         """Convert TFLite ZEROS LIKE"""
         try:

tests/python/frontend/tflite/test_forward.py

@@ -244,6 +244,57 @@ def test_forward_slice():
         _test_slice(np.arange(8, dtype=np.int32).reshape((2, 4)), begin=[0, 1], size=[-1, -1])
         _test_slice(np.arange(5, dtype=np.int32).reshape((5, )), begin=[4], size=[-1])
 
+#######################################################################
+# Gather
+# ------
+
+def _test_gather(dshape, indices, axis, dtype):
+    """ One iteration of Gather """
+    data = np.random.uniform(1, 10, size=dshape).astype(dtype)
+    indices = np.asarray(indices).astype('int32')
+
+    with tf.Graph().as_default():
+        in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
+        out = array_ops.gather(in_data, indices, axis=axis)
+        compare_tflite_with_tvm(data, 'Placeholder:0', [in_data], [out])
+
+def test_forward_gather():
+    """ GATHER """
+    _test_gather((4,), [1], None, 'float32')
+    _test_gather((1, 4), [0], 0, 'int32')
+    _test_gather((4,), [[[1, 0], [0, 1]]], 0, 'float32')
+    _test_gather((2, 2), [[[1, 0], [0, 1]]], 0, 'int32')
+    _test_gather((2, 2), [[[1, 0], [0, 1]]], 1, 'int32')
+    _test_gather((2, 2), [[[1, 0], [0, 1]]], 0, 'float32')
+    _test_gather((3, 3, 3),  [[[1, 0]]], 0, 'int32')
+    _test_gather((3, 3, 3), [[[1, 0]]], 2, 'int32')
+    _test_gather((4, 3, 5, 6),  [[2, 1, 0, 0]], 0, 'float32')
+
+#######################################################################
+# StridedSlice
+# ------------
+
+def _test_stridedslice(ip_shape, begin, end, stride, dtype,
+                       begin_mask=0, end_mask=0, new_axis_mask=0,
+                       shrink_axis_mask=0, ellipsis_mask=0):
+    """ One iteration of a Stridedslice """
+    data = np.random.uniform(size=ip_shape).astype(dtype)
+
+    with tf.Graph().as_default():
+        in_data = tf.placeholder(dtype, ip_shape, name="in_data")
+        out = array_ops.strided_slice(in_data, begin, end, stride,
+                     begin_mask=begin_mask,
+                     end_mask=end_mask, new_axis_mask=new_axis_mask,
+                     shrink_axis_mask=shrink_axis_mask,
+                     ellipsis_mask=ellipsis_mask)
+        compare_tflite_with_tvm(data, 'in_data:0', [in_data], [out])
+
+def test_forward_stridedslice():
+    '''test StridedSlice'''
+    _test_stridedslice((2), [1], [1], [1], 'float32', shrink_axis_mask=1)
+    _test_stridedslice((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1], 'float32')
+    _test_stridedslice((3, 4), [1, 0], [4, 4], [1, 1], 'float32', shrink_axis_mask=1)
+
 #######################################################################
 # transpose
 # ---------
@@ -1456,6 +1507,8 @@ def test_forward_mediapipe_hand_landmark():
     test_all_resize()
     test_forward_squeeze()
     test_forward_slice()
+    test_forward_gather()
+    test_forward_stridedslice()
 
     # NN
     test_forward_convolution()