Source code for aitemplate.compiler.ops.conv.conv2d_bias_add_hardswish

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"""
fused conv2d_bias_add_hardswish op, for residual block
"""
from aitemplate.compiler.ops.conv.common_conv2d_bias_add_activation import (
    conv2d_bias_add_activation,
)


# pylint: disable=C0103
[docs]class conv2d_bias_add_hardswish(conv2d_bias_add_activation): r"""Conv2d_bias_add_hardswish. Applies a 2D convolution on input in shape (N, H, W, C_in), adds a bias in shape (C_out), adds the residual in shape (N, H_out, W_out, C_out), performs hardswish operation and produces output in shape (N, H_out, W_out, C_out). N is batch size, H, W are the height and width of the input images in pixels, and C is the number of channels. Args: input: input tensor of shape :math:`(N , H , W, \text{in\_channels})` weight: filters of shape :math:`(\text{out\_channels} , K_h, K_w, \frac{\text{in\_channels}}{\text{groups}})` bias: optional bias tensor of shape :math:`(\text{out\_channels})` residual: residual to add after conv2d_bias This operator uses "channels_last" data format. Below is an example and its equivalence in PyTorch: .. highlight:: python .. code-block:: python X = Tensor(shape=[N, H, W, C_in], dtype="float16", name="images", is_input=True) W = Tensor(shape=[C_out, K_h, K_w, C_in], dtype="float16", name="weight", is_input=True) B = Tensor(shape=[C_out], dtype="float16", name="bias", is_input=True) R = Tensor(shape=[N, H_out, W_out, C_out], dtype="float16", name="residual", is_input=True) OP = aitemplate.compiler.ops.conv2d_bias_add_hardswish(stride=1, pad=1, dilate=1) Y = OP(X, W, B, R) .. highlight:: python .. code-block:: python X_pt = NHWC2NCHW(X_ait) W_pt = NHWC2NCHW(W_ait) B_pt = NHWC2NCHW(B_ait) R_pt = NHWC2NCHW(R_ait) Y_pt = torch.nn.functional.conv2d(X_pt, W_pt, bias=B_pt) Z_pt = Y_pt + R_pt Result_pt = torch.nn.functional.hardswish(Z_pt) Result = NCHW2NHWC(Result_pt) """ def __init__(self, stride, pad, dilate=1, group=1) -> None: """Conv2d_bias_add_hardswish constructor. Parameters ---------- stride : int Stride of the convolution pad : int Size of padding to add to the input dilate : int, optional Size of spacing between kernel elements, by default 1 group : int, optional Number of input channels to process to compute one output channel, by default 1 """ super().__init__("hardswish", stride, pad, dilate=dilate, group=group) def _get_op_attributes(self): attr = super()._get_op_attributes() del attr["activation"] return attr