Refactor torch frontend max pooling functions

ivy/functional/frontends/torch/nn/functional/pooling_functions.py

@@ -103,27 +103,26 @@ def avg_pool2d(
 
 
 @to_ivy_arrays_and_back
-def max_pool1d(input, kernel_size, stride=None, padding=0):
-    kernel_size = _broadcast_pooling_helper(kernel_size, "1d", name="kernel_size")
-    stride = _broadcast_pooling_helper(stride, "1d", name="stride")
-    padding = _broadcast_pooling_helper(padding, "1d", name="padding")
-    kernel_pads = list(zip(kernel_size, padding))
-
+def max_pool1d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    ceil_mode=False,
+    dilation=1,
+    return_indices=False,
+):
+    if stride is None:
+        stride = kernel_size
     data_format = "NCW"
-
-    if not all([pad <= kernel / 2 for kernel, pad in kernel_pads]):
-        raise ValueError(
-            "pad should be smaller than or equal to half of kernel size, "
-            f"but got padding={padding}, kernel_size={kernel_size}. "
-        )
-    # figure out whether to apply padding
-    if all([pad == ivy.ceil((kernel - 1) / 2) for kernel, pad in kernel_pads]):
-        padding_str = "SAME"
-    else:
-        padding_str = "VALID"
-
     return ivy.max_pool1d(
-        input, kernel_size, stride, padding_str, data_format=data_format
+        input,
+        kernel_size,
+        stride,
+        padding,
+        data_format=data_format,
+        dilation=dilation,
+        ceil_mode=ceil_mode,
     )
 
 
@@ -138,14 +137,9 @@ def max_pool2d(
     ceil_mode=False,
     return_indices=False,
 ):
-    # ToDo: Add return_indices once superset in implemented
-    dim_check = False
-    if input.ndim == 3:
-        input = input.expand_dims()
-        dim_check = True
-    if not stride:
+    if stride is None:
         stride = kernel_size
-    ret = ivy.max_pool2d(
+    return ivy.max_pool2d(
         input,
         kernel_size,
         stride,
@@ -154,9 +148,6 @@ def max_pool2d(
         dilation=dilation,
         ceil_mode=ceil_mode,
     )
-    if dim_check:
-        return ret.squeeze(0)
-    return ret
 
 
 @to_ivy_arrays_and_back

ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_pooling_functions.py

@@ -36,7 +36,7 @@ def calculate_same_padding(kernel_size, stride, shape):
     if all([kernel_size[i] / 2 >= padding[i] for i in range(len(kernel_size))]):
         if is_same_padding(padding, stride, kernel_size, shape):
             return padding
-    return (0, 0)
+    return [0] * len(shape)
 
 
 # avg_pool1d
@@ -200,8 +200,8 @@ def test_torch_avg_pool3d(
         max_dims=3,
         min_side=1,
         max_side=3,
-        explicit_or_str_padding=False,
         only_explicit_padding=True,
+        data_format="channel_first",
     ),
     test_with_out=st.just(False),
 )
@@ -215,23 +215,7 @@ def test_torch_max_pool1d(
     on_device,
 ):
     input_dtype, x, kernel_size, stride, padding = dtype_x_k_s
-
-    # Torch ground truth func expects input to be consistent
-    # with a channels first format i.e. NCW
-    x[0] = x[0].reshape((x[0].shape[0], x[0].shape[-1], x[0].shape[1]))
-    x_shape = [x[0].shape[2]]
-
-    # Torch ground truth func also takes padding input as an integer
-    # or a tuple of integers, not a string
-    padding = tuple(
-        [
-            ivy.functional.layers._handle_padding(
-                x_shape[i], stride[0], kernel_size[i], padding
-            )
-            for i in range(len(x_shape))
-        ]
-    )
-
+    padding = (padding[0][0], )
     helpers.test_frontend_function(
         input_dtypes=input_dtype,
         backend_to_test=backend_fw,
@@ -254,9 +238,10 @@ def test_torch_max_pool1d(
         max_dims=4,
         min_side=1,
         max_side=4,
-        explicit_or_str_padding=True,
+        only_explicit_padding=True,
         return_dilation=True,
-    ).filter(lambda x: x[4] != "VALID" and x[4] != "SAME"),
+        data_format="channel_first",
+    ),
     test_with_out=st.just(False),
     ceil_mode=st.just(True),
 )
@@ -271,9 +256,6 @@ def test_torch_max_pool2d(
     on_device,
 ):
     dtype, x, kernel, stride, pad, dilation = x_k_s_p
-    # Torch ground truth func expects input to be consistent
-    # with a channels first format i.e. NCHW
-    x[0] = x[0].reshape((x[0].shape[0], x[0].shape[-1], *x[0].shape[1:-1]))
     pad = (pad[0][0], pad[1][0])
 
     helpers.test_frontend_function(