[relax][frontend][tflite] Add tests for fully_connected/depthwise_conv2d/transpose_conv/l2_pool2d

tests/python/relax/test_frontend_tflite.py

@@ -707,6 +707,73 @@ def main(x: R.Tensor((5, 30), dtype="float32")) -> R.Tensor(out_shape, dtype="in
     verify(TfInput, Expected)
 
 
+def test_fully_connected():
+    class FullyConnected(tf.Module):
+        @tf.function(input_signature=[tf.TensorSpec(shape=(1, 8), dtype=tf.float32)])
+        def func(self, x):
+            weight = tf.constant(np.arange(24, dtype=np.float32).reshape((3, 8)))
+            bias = tf.constant(np.array([0.5, 1.0, -1.0], dtype=np.float32))
+            out = tf.matmul(x, weight, transpose_b=True)
+            return tf.nn.bias_add(out, bias)
+
+    verify(FullyConnected)
+
+
+def test_depthwise_conv2d():
+    class DepthwiseConv2D(tf.Module):
+        @tf.function(
+            input_signature=[
+                tf.TensorSpec(shape=(1, 8, 8, 2), dtype=tf.float32),
+                tf.TensorSpec(shape=(3, 3, 2, 1), dtype=tf.float32),
+            ]
+        )
+        def func(self, data, kernel):
+            return tf.nn.depthwise_conv2d(
+                input=data,
+                filter=kernel,
+                strides=[1, 1, 1, 1],
+                padding="SAME",
+            )
+
+    verify(DepthwiseConv2D)
+
+
+def test_transpose_conv():
+    class TransposeConv(tf.Module):
+        @tf.function(
+            input_signature=[
+                tf.TensorSpec(shape=(1, 8, 8, 2), dtype=tf.float32),
+                tf.TensorSpec(shape=(3, 3, 3, 2), dtype=tf.float32),
+            ]
+        )
+        def func(self, data, kernel):
+            output_shape = tf.constant([1, 8, 8, 3], dtype=tf.int32)
+            return tf.nn.conv2d_transpose(
+                input=data,
+                filters=kernel,
+                output_shape=output_shape,
+                strides=[1, 1, 1, 1],
+                padding="SAME",
+            )
+
+    verify(TransposeConv)
+
+
+def test_l2_pool2d():
+    class L2Pool2D(tf.Module):
+        @tf.function(input_signature=[tf.TensorSpec(shape=(1, 8, 8, 2), dtype=tf.float32)])
+        def func(self, data):
+            return tf.nn.pool(
+                input=data,
+                window_shape=[2, 2],
+                pooling_type="AVG",
+                strides=[1, 1],
+                padding="SAME",
+            )
+
+    verify(L2Pool2D)
+
+
 def _make_conv2d_module(data_shape, kernel_shape, data_format, strides, padding):
     class Conv2DModule(tf.Module):
         @tf.function(