siju-samuel commented on a change in pull request #5992:
URL: https://github.com/apache/incubator-tvm/pull/5992#discussion_r452186951
##########
File path: python/tvm/relay/frontend/tflite.py
##########
@@ -1634,6 +1636,54 @@ def convert_reduce_sum(self, op):
def convert_reduce_any(self, op):
return self._convert_reduce(_op.reduce.any, op)
+ def _convert_arg_min_max(self, relay_op, op):
+ """Generic method to convert TFLite arg_min_max"""
+ try:
+ from tflite.BuiltinOptions import BuiltinOptions
+ from tflite.ArgMinOptions import ArgMinOptions
+ from tflite.ArgMaxOptions import ArgMaxOptions
+ except ImportError:
+ raise ImportError("The tflite package must be installed")
+
+ input_tensors = self.get_input_tensors(op)
+ assert len(input_tensors) == 2, "two input tensor arguments expected"
+
+ output_tensors = self.get_output_tensors(op)
+ assert len(output_tensors) == 1, "one output tensor expected"
+
+ input_tensor = input_tensors[0]
+ in_expr = self.get_expr(input_tensor.tensor_idx)
+ axis_tensor = input_tensors[1]
+ # In Tensorflow, `axis` argument is a Tensor, not attribute. We
+ # support the case where it inputs from a scalar constant.
+ axis_value = self.get_tensor_value(axis_tensor)
+ assert axis_value.size == 1
+ axis_value = axis_value.item()
+
+ if op.BuiltinOptionsType() == BuiltinOptions.ArgMinOptions:
+ arg_min_max_options = ArgMinOptions()
+ elif op.BuiltinOptionsType() == BuiltinOptions.ArgMaxOptions:
+ arg_min_max_options = ArgMaxOptions()
+ op_options = op.BuiltinOptions()
+ arg_min_max_options.Init(op_options.Bytes, op_options.Pos)
+
+ # set keepdims to True since tflite 1.13 removes all dims of size 1
+ # WARNING: all other versions of tflite > 1.13 need keepdims=False
+ out = relay_op(in_expr, axis=axis_value, keepdims=False, exclude=False)
+
+ return out
+
+ def convert_arg_min(self, op):
+ """Convert TFLite ARG_MIN"""
+ if self.is_quantized(op):
+ raise tvm.error.OpNotImplemented(
+ 'TFlite quantized ARG_MIN operator is not supported yet.')
Review comment:
Add the same check for `arg_max` as well or can do the check in
`_convert_arg_min_max`
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -1755,6 +1755,39 @@ def test_all_reduce():
if package_version.parse(tf.VERSION) >= package_version.parse('1.15.0'):
_test_forward_reduce(_test_reduce_any, dtype="bool")
+#######################################################################
+# Arg_min_max
+# -----------
+
+def _test_arg_min_max(math_op, data, axis, quantized=False):
+ """ One iteration of arg_min_max"""
+
+ with tf.Graph().as_default():
+ t_name="in"
+ in_data = array_ops.placeholder(shape=data.shape, dtype=np.float32,
name=t_name )
+ input_range=None
+ qmin, qmax = -100, 102
+ if quantized:
+ inq_data = tf.quantization.fake_quant_with_min_max_args(in_data,
min=qmin, max=qmax, name= 'q' + t_name )
+ input_range = { inq_data.name.split(':')[0]: (qmin, qmax)}
+ out = math_op(input=inq_data, axis=axis)
+ compare_tflite_with_tvm([data], [inq_data.name], [inq_data],
[out], quantized=True, input_range=input_range)
+ else:
+ out = math_op(input=in_data, axis=axis)
+ compare_tflite_with_tvm([data], [in_data.name], [in_data], [out])
+
+def test_forward_arg_min_max():
+ data = np.array(np.random.uniform(0, 100, (3, 4)), dtype=np.uint8)
+ # test quantized
+ # There is no quantized version of ArgMin
+ for axis in [None, 0, 1, -1]:
+ _test_arg_min_max(math_ops.argmax, data, axis, True)
+
+ data = np.array(np.random.uniform(0, 100, (3, 4)), dtype=np.float32)
Review comment:
include -ve and float numbers here.
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