[Frontend][TFLite] L2_POOL_2D operator (#5452)

* TFLITE fill and splitv ops

* l2_pool_2d op changes in comment

* TFLite l2_pool_2d op added test case in main

* TFLite L2_POOL_2D added check for quantized input

python/tvm/relay/frontend/tflite.py

@@ -87,6 +87,7 @@ class OperatorConverter(object):
             'GREATER': self.convert_greater,
             'HARD_SWISH': self.convert_hard_swish,
             'L2_NORMALIZATION': self.convert_l2_normalization,
+            'L2_POOL_2D': self.convert_l2_pool2d,
             'LESS_EQUAL': self.convert_less_equal,
             'LESS': self.convert_less,
             'LOCAL_RESPONSE_NORMALIZATION': self.convert_lrn,
@@ -332,6 +333,10 @@ class OperatorConverter(object):
         """Convert TFLite max pool2d"""
         return self.convert_pool2d(op, "max")
 
+    def convert_l2_pool2d(self, op):
+        """Convert TFLite l2 pool2d"""
+        return self.convert_pool2d(op, "l2")
+
     def convert_reshape(self, op):
         """Convert TFLite reshape"""
         try:
@@ -1770,6 +1775,16 @@ class OperatorConverter(object):
                 assert self.has_same_qnn_params(input_tensor, output_tensor), \
                         "qnn.op.max_pool2d requires input and output qnn params to be same"
             out = _op.nn.max_pool2d(in_expr, **params)
+        elif pool_type == "l2":
+            # L2_POOL_2D is equivalent to square_root(avg_pool(square(in_data)))
+            # TFLite does not have support for quantised L2_POOL_2D op.
+            assert not input_tensor.qnn_params, \
+                "As TFLite does not have support for quantized L2_POOL_2D, \
+                Quantized input is not expected."
+            exp_type = self.get_tensor_type_str(output_tensor.tensor.Type())
+            square_exp = _op.power(in_expr, relay.const(2, exp_type))
+            avg_pool_exp = _op.nn.avg_pool2d(square_exp, **params)
+            out = _op.sqrt(avg_pool_exp)
         else:
             raise tvm.error.OpNotImplemented(
                 'Operator {} is not supported for frontend TFLite.'.format(pool_type + ' pool'))

tests/python/frontend/tflite/test_forward.py

@@ -561,6 +561,31 @@ def test_forward_pooling():
                       strides=[2, 1])
 
 
+def _test_l2_pool2d(input_shape, ksize, strides, padding, data_format, fused_func_name=None):
+    x = np.arange(np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
+
+    with tf.Graph().as_default():
+        in_data = tf.placeholder(
+            dtype=tf.float32, name="input", shape=input_shape)
+        out = tf.sqrt(tf.nn.avg_pool(
+            tf.square(in_data), ksize=ksize, strides=strides,
+            padding=padding, data_format=data_format))
+        out = with_fused_activation_function(out, fused_func_name)
+
+        compare_tflite_with_tvm(x, 'input', [in_data], [out])
+
+
+def test_forward_l2_pool2d():
+    _test_l2_pool2d([1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], 'SAME', "NHWC", "RELU6")
+    _test_l2_pool2d([2, 9, 10, 2], [1, 1, 1, 1], [1, 1, 1, 1], 'SAME', "NHWC", "RELU6")
+    _test_l2_pool2d([2, 9, 10, 2], [1, 2, 1, 1], [1, 1, 1, 1], 'SAME', "NHWC")
+    _test_l2_pool2d([2, 9, 10, 2], [1, 2, 1, 1], [1, 1, 2, 1], 'SAME', "NHWC")
+    _test_l2_pool2d([1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], 'VALID', "NHWC", "RELU")
+    _test_l2_pool2d([2, 9, 10, 2], [1, 1, 1, 1], [1, 1, 1, 1], 'VALID', "NHWC")
+    _test_l2_pool2d([2, 9, 10, 2], [1, 2, 1, 1], [1, 1, 1, 1], 'VALID', "NHWC")
+    _test_l2_pool2d([2, 9, 10, 2], [1, 2, 1, 1], [1, 1, 2, 1], 'VALID', "NHWC", "RELU6")
+
+
 #######################################################################
 # Convolution
 # -----------
@@ -1938,6 +1963,7 @@ if __name__ == '__main__':
     test_forward_transpose_conv()
     test_forward_logistic()
     test_forward_pooling()
+    test_forward_l2_pool2d()
     test_forward_softmax()
     test_forward_tanh()
     test_forward_relu()