'PACK': self.convert_pack,
'LOGISTIC': self.convert_logistic,
'SPLIT': self.convert_split,
- 'TRANSPOSE': self.convert_transpose
+ 'TRANSPOSE': self.convert_transpose,
+ 'TILE': self.convert_tile
}
def check_unsupported_ops(self):
return out
+ def convert_tile(self, op):
+ """tile implementation."""
+ try:
+ from tflite.Operator import Operator
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ assert isinstance(op, Operator)
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 2, "input tensors length should be 2"
+ input_tensor = input_tensors[0]
+ input_tensor_idx = input_tensor.tensor_idx
+
+ in_expr = self.get_expr(input_tensor_idx)
+
+ # reps (tuple of int) – The number of times repeating the tensor data.
+ reps = tuple(self.get_tensor_value(input_tensors[1]))
+
+ out = _op.tile(in_expr, reps)
+
+ return out
+
def convert_pool2d(self, op, pool_type):
"""pool2d implementation."""
try:
_test_forward_transpose((2, 3, 4, 5), (3, 0, 1, 2))
_test_forward_transpose((2, 3, 4, 5), ())
+#######################################################################
+# tile
+# ---------
+
+
+def _test_forward_tile(in_shape, reps, dtype):
+ data = np.random.uniform(-5, 5, size=in_shape).astype(dtype)
+
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
+
+ out = array_ops.tile(in_data, reps)
+
+ compare_tflite_with_tvm(data, 'Placeholder:0', [in_data], [out])
+
+
+def test_forward_tile():
+ _test_forward_tile((2, ), (3, ), "int32")
+ _test_forward_tile((2, 2), (2, 3), "float32")
+
#######################################################################
# Pooling
# Transpose
test_forward_transpose()
+ # Tile
+ test_forward_tile()
+
# Transforms
test_forward_concatenation()
test_forward_pad()