File size: 16,287 Bytes
375a436
 
 
 
 
 
 
 
 
 
 
 
 
 
2506395
375a436
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2506395
375a436
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
# 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.

"""Custom PyTorch ops for efficient resampling of 2D images."""

import os
import warnings
import numpy as np
import torch
import traceback

from .. import custom_ops
from .. import misc
from . import conv2d_gradfix

#----------------------------------------------------------------------------

_inited = False
_plugin = None

def _init():
    global _inited, _plugin
    if not _inited:
        sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
        try:
            _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
        except:
            warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
    return _plugin is not None

def _parse_scaling(scaling):
    if isinstance(scaling, int):
        scaling = [scaling, scaling]
    assert isinstance(scaling, (list, tuple))
    assert all(isinstance(x, int) for x in scaling)
    sx, sy = scaling
    assert sx >= 1 and sy >= 1
    return sx, sy

def _parse_padding(padding):
    if isinstance(padding, int):
        padding = [padding, padding]
    assert isinstance(padding, (list, tuple))
    assert all(isinstance(x, int) for x in padding)
    if len(padding) == 2:
        padx, pady = padding
        padding = [padx, padx, pady, pady]
    padx0, padx1, pady0, pady1 = padding
    return padx0, padx1, pady0, pady1

def _get_filter_size(f):
    if f is None:
        return 1, 1
    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
    fw = f.shape[-1]
    fh = f.shape[0]
    with misc.suppress_tracer_warnings():
        fw = int(fw)
        fh = int(fh)
    misc.assert_shape(f, [fh, fw][:f.ndim])
    assert fw >= 1 and fh >= 1
    return fw, fh

#----------------------------------------------------------------------------

def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.

    Args:
        f:           Torch tensor, numpy array, or python list of the shape
                     `[filter_height, filter_width]` (non-separable),
                     `[filter_taps]` (separable),
                     `[]` (impulse), or
                     `None` (identity).
        device:      Result device (default: cpu).
        normalize:   Normalize the filter so that it retains the magnitude
                     for constant input signal (DC)? (default: True).
        flip_filter: Flip the filter? (default: False).
        gain:        Overall scaling factor for signal magnitude (default: 1).
        separable:   Return a separable filter? (default: select automatically).

    Returns:
        Float32 tensor of the shape
        `[filter_height, filter_width]` (non-separable) or
        `[filter_taps]` (separable).
    """
    # Validate.
    if f is None:
        f = 1
    f = torch.as_tensor(f, dtype=torch.float32)
    assert f.ndim in [0, 1, 2]
    assert f.numel() > 0
    if f.ndim == 0:
        f = f[np.newaxis]

    # Separable?
    if separable is None:
        separable = (f.ndim == 1 and f.numel() >= 8)
    if f.ndim == 1 and not separable:
        f = f.ger(f)
    assert f.ndim == (1 if separable else 2)

    # Apply normalize, flip, gain, and device.
    if normalize:
        f /= f.sum()
    if flip_filter:
        f = f.flip(list(range(f.ndim)))
    f = f * (gain ** (f.ndim / 2))
    f = f.to(device=device)
    return f

#----------------------------------------------------------------------------

def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
    r"""Pad, upsample, filter, and downsample a batch of 2D images.

    Performs the following sequence of operations for each channel:

    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).

    2. Pad the image with the specified number of zeros on each side (`padding`).
       Negative padding corresponds to cropping the image.

    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
       so that the footprint of all output pixels lies within the input image.

    4. Downsample the image by keeping every Nth pixel (`down`).

    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
    The fused op is considerably more efficient than performing the same calculation
    using standard PyTorch ops. It supports gradients of arbitrary order.

    Args:
        x:           Float32/float64/float16 input tensor of the shape
                     `[batch_size, num_channels, in_height, in_width]`.
        f:           Float32 FIR filter of the shape
                     `[filter_height, filter_width]` (non-separable),
                     `[filter_taps]` (separable), or
                     `None` (identity).
        up:          Integer upsampling factor. Can be a single int or a list/tuple
                     `[x, y]` (default: 1).
        down:        Integer downsampling factor. Can be a single int or a list/tuple
                     `[x, y]` (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).
        flip_filter: False = convolution, True = correlation (default: False).
        gain:        Overall scaling factor for signal magnitude (default: 1).
        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).

    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    """
    assert isinstance(x, torch.Tensor)
    assert impl in ['ref', 'cuda']
    if impl == 'cuda' and x.device.type == 'cuda' and _init():
        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)

#----------------------------------------------------------------------------

@misc.profiled_function
def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
    """
    # Validate arguments.
    assert isinstance(x, torch.Tensor) and x.ndim == 4
    if f is None:
        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
    assert f.dtype == torch.float32 and not f.requires_grad
    batch_size, num_channels, in_height, in_width = x.shape
    upx, upy = _parse_scaling(up)
    downx, downy = _parse_scaling(down)
    padx0, padx1, pady0, pady1 = _parse_padding(padding)

    # Upsample by inserting zeros.
    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])

    # Pad or crop.
    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]

    # Setup filter.
    f = f * (gain ** (f.ndim / 2))
    f = f.to(x.dtype)
    if not flip_filter:
        f = f.flip(list(range(f.ndim)))

    # Convolve with the filter.
    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
    if f.ndim == 4:
        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
    else:
        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)

    # Downsample by throwing away pixels.
    x = x[:, :, ::downy, ::downx]
    return x

#----------------------------------------------------------------------------

_upfirdn2d_cuda_cache = dict()

def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
    """
    # Parse arguments.
    upx, upy = _parse_scaling(up)
    downx, downy = _parse_scaling(down)
    padx0, padx1, pady0, pady1 = _parse_padding(padding)

    # Lookup from cache.
    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
    if key in _upfirdn2d_cuda_cache:
        return _upfirdn2d_cuda_cache[key]

    # Forward op.
    class Upfirdn2dCuda(torch.autograd.Function):
        @staticmethod
        def forward(ctx, x, f): # pylint: disable=arguments-differ
            assert isinstance(x, torch.Tensor) and x.ndim == 4
            if f is None:
                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
            y = x
            if f.ndim == 2:
                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
            else:
                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
            ctx.save_for_backward(f)
            ctx.x_shape = x.shape
            return y

        @staticmethod
        def backward(ctx, dy): # pylint: disable=arguments-differ
            f, = ctx.saved_tensors
            _, _, ih, iw = ctx.x_shape
            _, _, oh, ow = dy.shape
            fw, fh = _get_filter_size(f)
            p = [
                fw - padx0 - 1,
                iw * upx - ow * downx + padx0 - upx + 1,
                fh - pady0 - 1,
                ih * upy - oh * downy + pady0 - upy + 1,
            ]
            dx = None
            df = None

            if ctx.needs_input_grad[0]:
                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)

            assert not ctx.needs_input_grad[1]
            return dx, df

    # Add to cache.
    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
    return Upfirdn2dCuda

#----------------------------------------------------------------------------

def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
    r"""Filter a batch of 2D images using the given 2D FIR filter.

    By default, the result is padded so that its shape matches the input.
    User-specified padding is applied on top of that, with negative values
    indicating cropping. Pixels outside the image are assumed to be zero.

    Args:
        x:           Float32/float64/float16 input tensor of the shape
                     `[batch_size, num_channels, in_height, in_width]`.
        f:           Float32 FIR filter of the shape
                     `[filter_height, filter_width]` (non-separable),
                     `[filter_taps]` (separable), or
                     `None` (identity).
        padding:     Padding with respect to the output. Can be a single number or a
                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
                     (default: 0).
        flip_filter: False = convolution, True = correlation (default: False).
        gain:        Overall scaling factor for signal magnitude (default: 1).
        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).

    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    """
    padx0, padx1, pady0, pady1 = _parse_padding(padding)
    fw, fh = _get_filter_size(f)
    p = [
        padx0 + fw // 2,
        padx1 + (fw - 1) // 2,
        pady0 + fh // 2,
        pady1 + (fh - 1) // 2,
    ]
    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)

#----------------------------------------------------------------------------

def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
    r"""Upsample a batch of 2D images using the given 2D FIR filter.

    By default, the result is padded so that its shape is a multiple of the input.
    User-specified padding is applied on top of that, with negative values
    indicating cropping. Pixels outside the image are assumed to be zero.

    Args:
        x:           Float32/float64/float16 input tensor of the shape
                     `[batch_size, num_channels, in_height, in_width]`.
        f:           Float32 FIR filter of the shape
                     `[filter_height, filter_width]` (non-separable),
                     `[filter_taps]` (separable), or
                     `None` (identity).
        up:          Integer upsampling factor. Can be a single int or a list/tuple
                     `[x, y]` (default: 1).
        padding:     Padding with respect to the output. Can be a single number or a
                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
                     (default: 0).
        flip_filter: False = convolution, True = correlation (default: False).
        gain:        Overall scaling factor for signal magnitude (default: 1).
        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).

    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    """
    upx, upy = _parse_scaling(up)
    padx0, padx1, pady0, pady1 = _parse_padding(padding)
    fw, fh = _get_filter_size(f)
    p = [
        padx0 + (fw + upx - 1) // 2,
        padx1 + (fw - upx) // 2,
        pady0 + (fh + upy - 1) // 2,
        pady1 + (fh - upy) // 2,
    ]
    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)

#----------------------------------------------------------------------------

def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
    r"""Downsample a batch of 2D images using the given 2D FIR filter.

    By default, the result is padded so that its shape is a fraction of the input.
    User-specified padding is applied on top of that, with negative values
    indicating cropping. Pixels outside the image are assumed to be zero.

    Args:
        x:           Float32/float64/float16 input tensor of the shape
                     `[batch_size, num_channels, in_height, in_width]`.
        f:           Float32 FIR filter of the shape
                     `[filter_height, filter_width]` (non-separable),
                     `[filter_taps]` (separable), or
                     `None` (identity).
        down:        Integer downsampling factor. Can be a single int or a list/tuple
                     `[x, y]` (default: 1).
        padding:     Padding with respect to the input. Can be a single number or a
                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
                     (default: 0).
        flip_filter: False = convolution, True = correlation (default: False).
        gain:        Overall scaling factor for signal magnitude (default: 1).
        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).

    Returns:
        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
    """
    downx, downy = _parse_scaling(down)
    padx0, padx1, pady0, pady1 = _parse_padding(padding)
    fw, fh = _get_filter_size(f)
    p = [
        padx0 + (fw - downx + 1) // 2,
        padx1 + (fw - downx) // 2,
        pady0 + (fh - downy + 1) // 2,
        pady1 + (fh - downy) // 2,
    ]
    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)

#----------------------------------------------------------------------------