File size: 8,128 Bytes
2f85de4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# python3.7

# 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.

Please refer to https://github.com/NVlabs/stylegan2-ada-pytorch
"""

# pylint: disable=line-too-long

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, impl='cuda'):
    """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, impl=impl)
            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, impl=impl)

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

@misc.profiled_function
def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False, impl='cuda'):
    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).
        impl:           Implementation mode of customized ops. 'ref' for native PyTorch
                        implementation, 'cuda' for `.cu` implementation
                        (default: 'cuda').

    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, impl=impl)
        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, impl=impl)
        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, impl=impl)
        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter, impl=impl)
        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, impl=impl)
        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight, impl=impl)
        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), impl=impl)
        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter, impl=impl)
        if down > 1:
            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter, impl=impl)
        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, impl=impl)

    # 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, impl=impl)
    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight, impl=impl)
    if down > 1:
        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter, impl=impl)
    return x

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

# pylint: enable=line-too-long