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| # Copyright (c) SenseTime Research. All rights reserved. | |
| # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| """2D convolution with optional up/downsampling.""" | |
| import torch | |
| from .. import misc | |
| from . import conv2d_gradfix | |
| from . import upfirdn2d | |
| from .upfirdn2d import _parse_padding | |
| from .upfirdn2d import _get_filter_size | |
| #---------------------------------------------------------------------------- | |
| def _get_weight_shape(w): | |
| with misc.suppress_tracer_warnings(): # this value will be treated as a constant | |
| shape = [int(sz) for sz in w.shape] | |
| misc.assert_shape(w, shape) | |
| return shape | |
| #---------------------------------------------------------------------------- | |
| def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): | |
| """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. | |
| """ | |
| out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) | |
| # Flip weight if requested. | |
| if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). | |
| w = w.flip([2, 3]) | |
| # Workaround performance pitfall in cuDNN 8.0.5, triggered when using | |
| # 1x1 kernel + memory_format=channels_last + less than 64 channels. | |
| if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose: | |
| if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: | |
| if out_channels <= 4 and groups == 1: | |
| in_shape = x.shape | |
| x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) | |
| x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) | |
| else: | |
| x = x.to(memory_format=torch.contiguous_format) | |
| w = w.to(memory_format=torch.contiguous_format) | |
| x = conv2d_gradfix.conv2d(x, w, groups=groups) | |
| return x.to(memory_format=torch.channels_last) | |
| # Otherwise => execute using conv2d_gradfix. | |
| op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d | |
| return op(x, w, stride=stride, padding=padding, groups=groups) | |
| #---------------------------------------------------------------------------- | |
| def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): | |
| r"""2D convolution with optional up/downsampling. | |
| Padding is performed only once at the beginning, not between the operations. | |
| Args: | |
| x: Input tensor of shape | |
| `[batch_size, in_channels, in_height, in_width]`. | |
| w: Weight tensor of shape | |
| `[out_channels, in_channels//groups, kernel_height, kernel_width]`. | |
| f: Low-pass filter for up/downsampling. Must be prepared beforehand by | |
| calling upfirdn2d.setup_filter(). None = identity (default). | |
| up: Integer upsampling factor (default: 1). | |
| down: Integer downsampling factor (default: 1). | |
| padding: Padding with respect to the upsampled image. Can be a single number | |
| or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` | |
| (default: 0). | |
| groups: Split input channels into N groups (default: 1). | |
| flip_weight: False = convolution, True = correlation (default: True). | |
| flip_filter: False = convolution, True = correlation (default: False). | |
| Returns: | |
| Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. | |
| """ | |
| # Validate arguments. | |
| assert isinstance(x, torch.Tensor) and (x.ndim == 4) | |
| assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) | |
| assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) | |
| assert isinstance(up, int) and (up >= 1) | |
| assert isinstance(down, int) and (down >= 1) | |
| assert isinstance(groups, int) and (groups >= 1) | |
| out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) | |
| fw, fh = _get_filter_size(f) | |
| px0, px1, py0, py1 = _parse_padding(padding) | |
| # Adjust padding to account for up/downsampling. | |
| if up > 1: | |
| px0 += (fw + up - 1) // 2 | |
| px1 += (fw - up) // 2 | |
| py0 += (fh + up - 1) // 2 | |
| py1 += (fh - up) // 2 | |
| if down > 1: | |
| px0 += (fw - down + 1) // 2 | |
| px1 += (fw - down) // 2 | |
| py0 += (fh - down + 1) // 2 | |
| py1 += (fh - down) // 2 | |
| # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. | |
| if kw == 1 and kh == 1 and (down > 1 and up == 1): | |
| x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) | |
| x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) | |
| return x | |
| # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. | |
| if kw == 1 and kh == 1 and (up > 1 and down == 1): | |
| x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) | |
| x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) | |
| return x | |
| # Fast path: downsampling only => use strided convolution. | |
| if down > 1 and up == 1: | |
| x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) | |
| x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) | |
| return x | |
| # Fast path: upsampling with optional downsampling => use transpose strided convolution. | |
| if up > 1: | |
| if groups == 1: | |
| w = w.transpose(0, 1) | |
| else: | |
| w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) | |
| w = w.transpose(1, 2) | |
| w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) | |
| px0 -= kw - 1 | |
| px1 -= kw - up | |
| py0 -= kh - 1 | |
| py1 -= kh - up | |
| pxt = max(min(-px0, -px1), 0) | |
| pyt = max(min(-py0, -py1), 0) | |
| x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) | |
| x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) | |
| if down > 1: | |
| x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) | |
| return x | |
| # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. | |
| if up == 1 and down == 1: | |
| if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: | |
| return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) | |
| # Fallback: Generic reference implementation. | |
| x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) | |
| x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) | |
| if down > 1: | |
| x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) | |
| return x | |
| #---------------------------------------------------------------------------- | |