389 lines
16 KiB
Python
389 lines
16 KiB
Python
# 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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"""Custom PyTorch ops for efficient resampling of 2D images."""
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import os
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import numpy as np
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import torch
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from .. import custom_ops
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from .. import misc
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from . import conv2d_gradfix
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#----------------------------------------------------------------------------
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_plugin = None
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def _init():
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global _plugin
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if _plugin is None:
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_plugin = custom_ops.get_plugin(
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module_name='upfirdn2d_plugin',
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sources=['upfirdn2d.cpp', 'upfirdn2d.cu'],
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headers=['upfirdn2d.h'],
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source_dir=os.path.dirname(__file__),
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extra_cuda_cflags=['--use_fast_math'],
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)
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return True
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def _parse_scaling(scaling):
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if isinstance(scaling, int):
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scaling = [scaling, scaling]
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assert isinstance(scaling, (list, tuple))
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assert all(isinstance(x, int) for x in scaling)
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sx, sy = scaling
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assert sx >= 1 and sy >= 1
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return sx, sy
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def _parse_padding(padding):
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if isinstance(padding, int):
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padding = [padding, padding]
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assert isinstance(padding, (list, tuple))
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assert all(isinstance(x, int) for x in padding)
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if len(padding) == 2:
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padx, pady = padding
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padding = [padx, padx, pady, pady]
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padx0, padx1, pady0, pady1 = padding
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return padx0, padx1, pady0, pady1
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def _get_filter_size(f):
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if f is None:
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return 1, 1
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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fw = f.shape[-1]
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fh = f.shape[0]
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with misc.suppress_tracer_warnings():
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fw = int(fw)
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fh = int(fh)
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misc.assert_shape(f, [fh, fw][:f.ndim])
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assert fw >= 1 and fh >= 1
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return fw, fh
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#----------------------------------------------------------------------------
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def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
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r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
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Args:
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f: Torch tensor, numpy array, or python list of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable),
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`[]` (impulse), or
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`None` (identity).
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device: Result device (default: cpu).
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normalize: Normalize the filter so that it retains the magnitude
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for constant input signal (DC)? (default: True).
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flip_filter: Flip the filter? (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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separable: Return a separable filter? (default: select automatically).
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Returns:
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Float32 tensor of the shape
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`[filter_height, filter_width]` (non-separable) or
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`[filter_taps]` (separable).
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"""
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# Validate.
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if f is None:
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f = 1
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f = torch.as_tensor(f, dtype=torch.float32)
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assert f.ndim in [0, 1, 2]
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assert f.numel() > 0
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if f.ndim == 0:
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f = f[np.newaxis]
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# Separable?
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if separable is None:
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separable = (f.ndim == 1 and f.numel() >= 8)
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if f.ndim == 1 and not separable:
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f = f.ger(f)
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assert f.ndim == (1 if separable else 2)
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# Apply normalize, flip, gain, and device.
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if normalize:
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f /= f.sum()
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if flip_filter:
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f = f.flip(list(range(f.ndim)))
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f = f * (gain ** (f.ndim / 2))
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f = f.to(device=device)
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return f
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#----------------------------------------------------------------------------
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def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
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r"""Pad, upsample, filter, and downsample a batch of 2D images.
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Performs the following sequence of operations for each channel:
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1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
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2. Pad the image with the specified number of zeros on each side (`padding`).
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Negative padding corresponds to cropping the image.
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3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
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so that the footprint of all output pixels lies within the input image.
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4. Downsample the image by keeping every Nth pixel (`down`).
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This sequence of operations bears close resemblance to scipy.signal.upfirdn().
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The fused op is considerably more efficient than performing the same calculation
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using standard PyTorch ops. It supports gradients of arbitrary order.
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Args:
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x: Float32/float64/float16 input tensor of the shape
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`[batch_size, num_channels, in_height, in_width]`.
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f: Float32 FIR filter of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable), or
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`None` (identity).
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up: Integer upsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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down: Integer downsampling factor. Can be a single int or a list/tuple
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`[x, y]` (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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flip_filter: False = convolution, True = correlation (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
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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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assert isinstance(x, torch.Tensor)
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assert impl in ['ref', 'cuda']
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if impl == 'cuda' and x.device.type == 'cuda' and _init():
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return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
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return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
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#----------------------------------------------------------------------------
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@misc.profiled_function
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def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
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"""Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
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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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if f is None:
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f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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assert f.dtype == torch.float32 and not f.requires_grad
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batch_size, num_channels, in_height, in_width = x.shape
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upx, upy = _parse_scaling(up)
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downx, downy = _parse_scaling(down)
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padx0, padx1, pady0, pady1 = _parse_padding(padding)
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# Check that upsampled buffer is not smaller than the filter.
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upW = in_width * upx + padx0 + padx1
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upH = in_height * upy + pady0 + pady1
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assert upW >= f.shape[-1] and upH >= f.shape[0]
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# Upsample by inserting zeros.
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x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
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x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
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x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
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# Pad or crop.
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x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
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x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
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# Setup filter.
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f = f * (gain ** (f.ndim / 2))
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f = f.to(x.dtype)
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if not flip_filter:
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f = f.flip(list(range(f.ndim)))
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# Convolve with the filter.
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f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
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if f.ndim == 4:
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x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
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else:
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x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
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x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
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# Downsample by throwing away pixels.
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x = x[:, :, ::downy, ::downx]
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return x
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#----------------------------------------------------------------------------
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_upfirdn2d_cuda_cache = dict()
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def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
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"""Fast CUDA implementation of `upfirdn2d()` using custom ops.
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"""
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# Parse arguments.
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upx, upy = _parse_scaling(up)
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downx, downy = _parse_scaling(down)
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padx0, padx1, pady0, pady1 = _parse_padding(padding)
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# Lookup from cache.
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key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
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if key in _upfirdn2d_cuda_cache:
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return _upfirdn2d_cuda_cache[key]
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# Forward op.
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class Upfirdn2dCuda(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, f): # pylint: disable=arguments-differ
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assert isinstance(x, torch.Tensor) and x.ndim == 4
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if f is None:
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f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
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if f.ndim == 1 and f.shape[0] == 1:
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f = f.square().unsqueeze(0) # Convert separable-1 into full-1x1.
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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y = x
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if f.ndim == 2:
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y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
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else:
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y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0)
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y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain)
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ctx.save_for_backward(f)
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ctx.x_shape = x.shape
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return y
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@staticmethod
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def backward(ctx, dy): # pylint: disable=arguments-differ
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f, = ctx.saved_tensors
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_, _, ih, iw = ctx.x_shape
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_, _, oh, ow = dy.shape
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fw, fh = _get_filter_size(f)
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p = [
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fw - padx0 - 1,
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iw * upx - ow * downx + padx0 - upx + 1,
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fh - pady0 - 1,
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ih * upy - oh * downy + pady0 - upy + 1,
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]
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dx = None
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df = None
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if ctx.needs_input_grad[0]:
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dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
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assert not ctx.needs_input_grad[1]
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return dx, df
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# Add to cache.
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_upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
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return Upfirdn2dCuda
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#----------------------------------------------------------------------------
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def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
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r"""Filter a batch of 2D images using the given 2D FIR filter.
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By default, the result is padded so that its shape matches the input.
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User-specified padding is applied on top of that, with negative values
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indicating cropping. Pixels outside the image are assumed to be zero.
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Args:
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x: Float32/float64/float16 input tensor of the shape
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`[batch_size, num_channels, in_height, in_width]`.
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f: Float32 FIR filter of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable), or
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`None` (identity).
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padding: Padding with respect to the output. Can be a single number or a
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list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
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(default: 0).
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flip_filter: False = convolution, True = correlation (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
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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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padx0, padx1, pady0, pady1 = _parse_padding(padding)
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fw, fh = _get_filter_size(f)
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p = [
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padx0 + fw // 2,
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padx1 + (fw - 1) // 2,
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pady0 + fh // 2,
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pady1 + (fh - 1) // 2,
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]
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return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
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#----------------------------------------------------------------------------
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def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
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r"""Upsample a batch of 2D images using the given 2D FIR filter.
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By default, the result is padded so that its shape is a multiple of the input.
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User-specified padding is applied on top of that, with negative values
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indicating cropping. Pixels outside the image are assumed to be zero.
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Args:
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x: Float32/float64/float16 input tensor of the shape
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`[batch_size, num_channels, in_height, in_width]`.
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f: Float32 FIR filter of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable), or
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`None` (identity).
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up: Integer upsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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padding: Padding with respect to the output. Can be a single number or a
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list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
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(default: 0).
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flip_filter: False = convolution, True = correlation (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
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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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upx, upy = _parse_scaling(up)
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padx0, padx1, pady0, pady1 = _parse_padding(padding)
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fw, fh = _get_filter_size(f)
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p = [
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padx0 + (fw + upx - 1) // 2,
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padx1 + (fw - upx) // 2,
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pady0 + (fh + upy - 1) // 2,
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pady1 + (fh - upy) // 2,
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]
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return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
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#----------------------------------------------------------------------------
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def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
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r"""Downsample a batch of 2D images using the given 2D FIR filter.
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By default, the result is padded so that its shape is a fraction of the input.
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User-specified padding is applied on top of that, with negative values
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indicating cropping. Pixels outside the image are assumed to be zero.
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Args:
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x: Float32/float64/float16 input tensor of the shape
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`[batch_size, num_channels, in_height, in_width]`.
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f: Float32 FIR filter of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable), or
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`None` (identity).
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down: Integer downsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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padding: Padding with respect to the input. Can be a single number or a
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list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
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(default: 0).
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flip_filter: False = convolution, True = correlation (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
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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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downx, downy = _parse_scaling(down)
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padx0, padx1, pady0, pady1 = _parse_padding(padding)
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fw, fh = _get_filter_size(f)
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p = [
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padx0 + (fw - downx + 1) // 2,
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padx1 + (fw - downx) // 2,
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pady0 + (fh - downy + 1) // 2,
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pady1 + (fh - downy) // 2,
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]
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return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
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#----------------------------------------------------------------------------
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