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new e5d4c557e0 [relax][frontend][tflite] Add tests for
fully_connected/depthwise_conv2d/transpose_conv/l2_pool2d (#19372)
e5d4c557e0 is described below
commit e5d4c557e0481d6cce7825aca5bad5e875d21acf
Author: Ahmad Jahaf <[email protected]>
AuthorDate: Sun Apr 12 05:05:09 2026 +0300
[relax][frontend][tflite] Add tests for
fully_connected/depthwise_conv2d/transpose_conv/l2_pool2d (#19372)
This PR adds Relax TFLite frontend test coverage for:
- FULLY_CONNECTED
- DEPTHWISE_CONV_2D
- TRANSPOSE_CONV
- L2_POOL_2D
Part of fixing #18971.
---
.../tvm/relax/frontend/tflite/tflite_frontend.py | 2 -
tests/python/relax/test_frontend_tflite.py | 83 +++++++++++++++++++++-
2 files changed, 82 insertions(+), 3 deletions(-)
diff --git a/python/tvm/relax/frontend/tflite/tflite_frontend.py
b/python/tvm/relax/frontend/tflite/tflite_frontend.py
index 8b2f70a0f5..9c99e98e01 100644
--- a/python/tvm/relax/frontend/tflite/tflite_frontend.py
+++ b/python/tvm/relax/frontend/tflite/tflite_frontend.py
@@ -3120,8 +3120,6 @@ class OperatorConverter:
weight_expr_iohw,
strides=(stride_h, stride_w),
padding=padding,
- channels=int(out_channels),
- kernel_size=(int(kernel_h), int(kernel_w)),
data_layout="NHWC",
kernel_layout="IOHW",
out_dtype=output_tensor_type_str,
diff --git a/tests/python/relax/test_frontend_tflite.py
b/tests/python/relax/test_frontend_tflite.py
index 195d2f5542..58af46cbc9 100644
--- a/tests/python/relax/test_frontend_tflite.py
+++ b/tests/python/relax/test_frontend_tflite.py
@@ -710,6 +710,88 @@ def test_reduce(tf_op, relax_op, axis, out_shape):
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):
+ squared = tf.math.square(data)
+ pooled = tf.nn.avg_pool2d(squared, ksize=[2, 2], strides=[1, 1],
padding="SAME")
+ return tf.math.sqrt(pooled)
+
+ @I.ir_module
+ class Expected:
+ @R.function
+ def main(
+ data: R.Tensor((1, 8, 8, 2), dtype="float32")
+ ) -> R.Tensor((1, 8, 8, 2), dtype="float32"):
+ R.func_attr({"num_input": 1})
+ with R.dataflow():
+ squared = R.power(data, R.const(2.0, "float32"))
+ pooled = R.nn.avg_pool2d(
+ squared,
+ pool_size=[2, 2],
+ strides=[1, 1],
+ padding=[0, 0, 1, 1],
+ layout="NHWC",
+ )
+ gv = R.sqrt(pooled)
+ R.output(gv)
+ return gv
+
+ verify(L2Pool2D, Expected)
+
+
def test_l2_normalization():
class L2Normalization(tf.Module):
@tf.function(input_signature=[tf.TensorSpec(shape=(2, 4),
dtype=tf.float32)])
@@ -758,7 +840,6 @@ def test_reverse_v2():
verify(ReverseV2, Expected)
-
def _make_conv2d_module(data_shape, kernel_shape, data_format, strides,
padding):
class Conv2DModule(tf.Module):
@tf.function(
