ivy-llc#10505 : Conv2D Operations for TensorFlow frontend

ivy/functional/frontends/tensorflow/raw_ops.py

@@ -539,6 +539,35 @@ def EuclideanNorm(*, input, axis, keep_dims=False, name="EuclideanNorm"):
 ConcatV2 = to_ivy_arrays_and_back(map_raw_ops_alias(tf_frontend.concat))
 
 
+@to_ivy_arrays_and_back
+def Conv2D(
+    *,
+    input,
+    filter,
+    strides,
+    padding,
+    data_format="NHWC",
+    dilations=[1, 1, 1, 1],
+    name="Conv2D",
+):
+    if data_format == "NDHWC":
+        strides = [1] + strides[1:-1] + [1]
+        dilations = [1] + dilations[1:-1] + [1]
+    elif data_format == "NCDHW":
+        strides = [1, 1] + strides[2:] + [1]
+        dilations = [1, 1] + dilations[2:] + [1]
+
+    return tf_frontend.nn.conv2d(
+        input,
+        filter[:, :, None, :, :],
+        strides,
+        padding,
+        data_format=data_format,
+        dilations=dilations,
+        name=name,
+    )
+
+
 @to_ivy_arrays_and_back
 def Conv3D(
     *,

ivy_tests/test_ivy/test_frontends/test_tensorflow/test_raw_ops.py

@@ -2920,6 +2920,49 @@ def test_tensorflow_ConcatV2(
     )
 
 
+# Conv2D
+@handle_frontend_test(
+    fn_tree="tensorflow.nn.conv2d",
+    x_f_d_df=_x_and_filters(
+        dtypes=helpers.get_dtypes("float", full=False),
+        data_format=st.sampled_from(["NHWC"]),
+        padding=st.sampled_from(["SAME", "VALID"]),
+        type="2d",
+        dilation_min=1,
+        dilation_max=1,
+    ),
+    test_with_out=st.just(False),
+    number_positional_args=st.just(0),
+)
+def test_tensorflow_Conv2D(
+    *,
+    x_f_d_df,
+    test_flags,
+    frontend,
+    fn_tree,
+    on_device,
+):
+    input_dtype, x, filters, dilation, data_format, stride, padding = x_f_d_df
+
+    stride = _convolution_broadcast_helper(
+        stride, num_spatial_dims=2, channel_index=3, name="strides"
+    )
+
+    helpers.test_frontend_function(
+        input_dtypes=input_dtype,
+        test_flags=test_flags,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        input=x,
+        filter=filters[:, :, None, :],
+        strides=stride,
+        padding=padding,
+        data_format=data_format,
+        dilations=[1, 1, 1, 1],
+    )
+
+
 # Conv3D
 @handle_frontend_test(
     fn_tree="tensorflow.raw_ops.Conv3D",