fix(mge/functional): rm useless arguments

group and compute_mode are not used by local_conv2d

GitOrigin-RevId: 8e4f25bfd851d791f132ded0daddb3d636f65144

python_module/megengine/functional/__init__.py

@@ -65,6 +65,7 @@ from .nn import (
     interpolate,
     leaky_relu,
     linear,
+    local_conv2d,
     matrix_mul,
     max_pool2d,
     one_hot,

python_module/megengine/functional/nn.py

@@ -170,6 +170,34 @@ def conv_transpose2d(
     return res
 
 
+@wrap_io_tensor
+def local_conv2d(
+    inp: Tensor,
+    weight: Tensor,
+    stride: Union[int, Tuple[int, int]] = 1,
+    padding: Union[int, Tuple[int, int]] = 0,
+    dilation: Union[int, Tuple[int, int]] = 1,
+    conv_mode="CROSS_CORRELATION",
+) -> Tensor:
+    """Applies spatial 2D convolution over an image with untied kernels.
+
+    Refer to :class:`~.LocalConv2d` for more information.
+    """
+    ret = mgb.opr.group_local(
+        inp,
+        weight,
+        pad_h=padding[0],
+        pad_w=padding[1],
+        stride_h=stride[0],
+        stride_w=stride[1],
+        dilate_h=dilation[0],
+        dilate_w=dilation[1],
+        format="NCHW",
+        mode=conv_mode,
+    )
+    return ret
+
+
 @wrap_io_tensor
 def max_pool2d(
     inp: Tensor,

python_module/megengine/module/__init__.py

@@ -9,7 +9,7 @@
 from .activation import LeakyReLU, PReLU, ReLU, Sigmoid, Softmax
 from .batchnorm import BatchNorm1d, BatchNorm2d
 from .concat import Concat
-from .conv import Conv2d, ConvTranspose2d
+from .conv import Conv2d, ConvTranspose2d, LocalConv2d
 from .conv_bn_relu import ConvBn2d, ConvBnRelu2d
 from .dropout import Dropout
 from .elemwise import Elemwise

python_module/megengine/module/conv.py

@@ -14,7 +14,7 @@ import numpy as np
 import megengine._internal as mgb
 
 from ..core import Parameter
-from ..functional import conv2d, conv_transpose2d
+from ..functional import conv2d, conv_transpose2d, local_conv2d
 from ..utils.types import _pair, _pair_nonzero
 from . import init
 from .module import Module
@@ -224,7 +224,7 @@ class ConvTranspose2d(_ConvNd):
         ``in_channels`` and ``out_channels`` must be divisible by ``groups``,
         and there would be an extra dimension at the beginning of the weight's
         shape. Specifically, the shape of weight would be ``(groups,
-        out_channel // groups, in_channels // groups, *kernel_size)``. Default: 1
+        out_channels // groups, in_channels // groups, *kernel_size)``. Default: 1
     :param bias: wether to add a bias onto the result of convolution. Default:
         True
     :param conv_mode: Supports `CROSS_CORRELATION` or `CONVOLUTION`. Default:
@@ -306,3 +306,77 @@ class ConvTranspose2d(_ConvNd):
             self.conv_mode,
             self.compute_mode,
         )
+
+
+class LocalConv2d(Conv2d):
+    r"""Applies a spatial convolution with untied kernels over an input 4D tensor.
+    It is also known as the locally connected layer.
+
+    :param in_channels: number of input channels.
+    :param out_channels: number of output channels.
+    :param input_height: the height of the input images.
+    :param input_width: the width of the input images.
+    :param kernel_size: size of weight on spatial dimensions. If ``kernel_size`` is
+        an :class:`int`, the actual kernel size would be
+        ``(kernel_size, kernel_size)``. Default: 1
+    :param stride: stride of the 2D convolution operation. Default: 1
+    :param padding: size of the paddings added to the input on both sides of its
+        spatial dimensions. Only zero-padding is supported. Default: 0
+    :param groups: number of groups to divide input and output channels into,
+        so as to perform a "grouped convolution". When ``groups`` is not 1,
+        ``in_channels`` and ``out_channels`` must be divisible by ``groups``. 
+        The shape of weight is ``(groups, output_height, output_width,
+        in_channels // groups, *kernel_size, out_channels // groups)``.
+    """
+
+    _conv_mode_type = mgb.opr_param_defs.Convolution.Mode
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        input_height: int,
+        input_width: int,
+        kernel_size: Union[int, Tuple[int, int]],
+        stride: Union[int, Tuple[int, int]] = 1,
+        padding: Union[int, Tuple[int, int]] = 0,
+        dilation: Union[int, Tuple[int, int]] = 1,
+        groups: int = 1,
+        conv_mode: str = "CROSS_CORRELATION",
+    ):
+        self.input_height = input_height
+        self.input_width = input_width
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias=False,
+        )
+
+    def _infer_weight_shape(self):
+        group = self.groups
+        output_height = (
+            self.input_height + self.padding[0] * 2 - self.kernel_size[0]
+        ) // self.stride[0] + 1
+        output_width = (
+            self.input_width + self.padding[1] * 2 - self.kernel_size[1]
+        ) // self.stride[1] + 1
+        # Assume format is NCHW
+        return (
+            group,
+            output_height,
+            output_width,
+            self.in_channels // group,
+            self.kernel_size[0],
+            self.kernel_size[1],
+            self.out_channels // group,
+        )
+
+    def forward(self, inp):
+        return local_conv2d(
+            inp, self.weight, self.stride, self.padding, self.dilation, self.conv_mode
+        )

python_module/test/unit/module/test_conv.py

@@ -11,7 +11,7 @@ import itertools
 import numpy as np
 
 from megengine import Parameter, tensor
-from megengine.module import ConvTranspose2d
+from megengine.module import ConvTranspose2d, LocalConv2d
 from megengine.test import assertTensorClose
 
 
@@ -50,3 +50,61 @@ def test_conv_transpose2d():
     y = conv_transpose2d(tensor(inp))
 
     assertTensorClose(out, y.numpy(), max_err=2e-6)
+
+
+def test_local_conv2d():
+    batch_size = 10
+    in_channels = 4
+    out_channels = 8
+    input_height = 8
+    input_width = 8
+    kernel_size = 3
+    stride = 1
+    padding = 1
+    dilation = 1
+    groups = 1
+    local_conv2d = LocalConv2d(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        input_height=input_height,
+        input_width=input_width,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        dilation=dilation,
+        groups=groups,
+    )
+    inputs = np.random.normal(
+        size=(batch_size, in_channels, input_height, input_width)
+    ).astype(np.float32)
+    output_height = (input_height + padding * 2 - kernel_size) // stride + 1
+    output_width = (input_width + padding * 2 - kernel_size) // stride + 1
+    weights = np.random.normal(
+        size=(
+            groups,
+            output_height,
+            output_width,
+            in_channels // groups,
+            kernel_size,
+            kernel_size,
+            out_channels // groups,
+        )
+    ).astype(np.float32)
+    local_conv2d.weight = Parameter(weights)
+    outputs = local_conv2d(tensor(inputs))
+    # naive calculation use numpy
+    # only test output_height == input_height, output_width == input_width, group == 1
+    inputs = np.pad(inputs, ((0, 0), (0, 0), (1, 1), (1, 1)))
+    expected = np.zeros(
+        (batch_size, out_channels, output_height, output_width), dtype=np.float32,
+    )
+    for n, oc, oh, ow in itertools.product(
+        *map(range, [batch_size, out_channels, output_height, output_width])
+    ):
+        ih, iw = oh * stride, ow * stride
+        expected[n, oc, ih, iw] = np.sum(
+            inputs[n, :, ih : ih + kernel_size, iw : iw + kernel_size]
+            * weights[0, oh, ow, :, :, :, oc]
+        )
+
+    assertTensorClose(outputs.numpy(), expected, max_err=1e-5)