r"""Applies a spatial convolution with untied kernels over an groupped channeled input 4D tensor. It is also known as the locally connected layer. Args: inp: input feature map. weight: convolution kernel. weight usually has shape ``(out_channels, in_channels, hei
(
inp: Tensor,
weight: Tensor,
bias: Optional[Tensor] = None,
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",
)
| 521 | |
| 522 | |
| 523 | def local_conv2d( |
| 524 | inp: Tensor, |
| 525 | weight: Tensor, |
| 526 | bias: Optional[Tensor] = None, |
| 527 | stride: Union[int, Tuple[int, int]] = 1, |
| 528 | padding: Union[int, Tuple[int, int]] = 0, |
| 529 | dilation: Union[int, Tuple[int, int]] = 1, |
| 530 | conv_mode="cross_correlation", |
| 531 | ): |
| 532 | r"""Applies a spatial convolution with untied kernels over an groupped channeled input 4D tensor. |
| 533 | It is also known as the locally connected layer. |
| 534 | |
| 535 | Args: |
| 536 | inp: input feature map. |
| 537 | weight: convolution kernel. |
| 538 | weight usually has shape ``(out_channels, in_channels, height, width)``. |
| 539 | bias: bias added to the result of convolution (if given). |
| 540 | stride: stride of the 2D convolution operation. Default: 1 |
| 541 | padding: size of the paddings added to the input on both sides of its |
| 542 | spatial dimensions. Only zero-padding is supported. Default: 0 |
| 543 | dilation: dilation of the 2D convolution operation. Default: 1 |
| 544 | |
| 545 | Returns: |
| 546 | output tensor. |
| 547 | """ |
| 548 | assert ( |
| 549 | conv_mode.lower() == "cross_correlation" |
| 550 | or conv_mode.name == "CROSS_CORRELATION" |
| 551 | ) |
| 552 | |
| 553 | stride_h, stride_w = expand_hw(stride) |
| 554 | pad_h, pad_w = expand_hw(padding) |
| 555 | dilate_h, dilate_w = expand_hw(dilation) |
| 556 | |
| 557 | # local conv only support "dense" mode, but weight could contain group dimension. |
| 558 | op = builtin.GroupLocal( |
| 559 | stride_h=stride_h, |
| 560 | stride_w=stride_w, |
| 561 | pad_h=pad_h, |
| 562 | pad_w=pad_w, |
| 563 | dilate_h=dilate_h, |
| 564 | dilate_w=dilate_w, |
| 565 | mode=conv_mode, |
| 566 | sparse="dense", |
| 567 | ) |
| 568 | (output,) = apply(op, inp, weight) |
| 569 | if bias is not None: |
| 570 | output += bias |
| 571 | return output |
| 572 | |
| 573 | |
| 574 | def conv_transpose3d( |