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