Spaces:
Running on Zero
Running on Zero
File size: 9,850 Bytes
3d736fe | 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 | import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn
NUMEL_LIMIT = 2**30
def ceil_to_divisible(n: int, dividend: int) -> int:
return math.ceil(dividend / (dividend // n))
def chunked_avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
n_chunks = math.ceil(input.numel() / NUMEL_LIMIT)
if n_chunks == 1:
return F.avg_pool1d(input, kernel_size, stride, padding, ceil_mode, count_include_pad)
else:
l_in = input.shape[-1]
l_out = math.floor((l_in + 2 * padding - kernel_size) / stride + 1)
output_shape = list(input.shape)
output_shape[-1] = l_out
out_list = []
for inp_chunk in input.chunk(n_chunks, dim=0):
out_chunk = F.avg_pool1d(inp_chunk, kernel_size, stride, padding, ceil_mode, count_include_pad)
out_list.append(out_chunk)
return torch.cat(out_list, dim=0)
def chunked_interpolate(input, scale_factor):
output_shape = list(input.shape)
output_shape = output_shape[:2] + [int(i * scale_factor) for i in output_shape[2:]]
n_chunks = math.ceil(torch.Size(output_shape).numel() / NUMEL_LIMIT)
if n_chunks == 1:
return F.interpolate(input, scale_factor=scale_factor)
else:
out_list = []
n_chunks += 1
for inp_chunk in input.chunk(n_chunks, dim=1):
out_chunk = F.interpolate(inp_chunk, scale_factor=scale_factor)
out_list.append(out_chunk)
return torch.cat(out_list, dim=1)
def get_conv3d_output_shape(
input_shape: torch.Size, out_channels: int, kernel_size: list, stride: list, padding: int, dilation: list
) -> list:
output_shape = [out_channels]
if len(input_shape) == 5:
output_shape.insert(0, input_shape[0])
for i, d in enumerate(input_shape[-3:]):
d_out = math.floor((d + 2 * padding[i] - dilation[i] * (kernel_size[i] - 1) - 1) / stride[i] + 1)
output_shape.append(d_out)
return output_shape
def get_conv3d_n_chunks(numel: int, n_channels: int, numel_limit: int):
n_chunks = math.ceil(numel / numel_limit)
n_chunks = ceil_to_divisible(n_chunks, n_channels)
return n_chunks
def channel_chunk_conv3d(
input: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
stride: list,
padding: list,
dilation: list,
groups: int,
numel_limit: int,
):
out_channels, in_channels = weight.shape[:2]
kernel_size = weight.shape[2:]
output_shape = get_conv3d_output_shape(input.shape, out_channels, kernel_size, stride, padding, dilation)
n_in_chunks = get_conv3d_n_chunks(input.numel(), in_channels, numel_limit)
n_out_chunks = get_conv3d_n_chunks(
np.prod(output_shape),
out_channels,
numel_limit,
)
if n_in_chunks == 1 and n_out_chunks == 1:
return F.conv3d(input, weight, bias, stride, padding, dilation, groups)
# output = torch.empty(output_shape, device=input.device, dtype=input.dtype)
# outputs = output.chunk(n_out_chunks, dim=1)
input_shards = input.chunk(n_in_chunks, dim=1)
weight_chunks = weight.chunk(n_out_chunks)
output_list = []
if bias is not None:
bias_chunks = bias.chunk(n_out_chunks)
else:
bias_chunks = [None] * n_out_chunks
for weight_, bias_ in zip(weight_chunks, bias_chunks):
weight_shards = weight_.chunk(n_in_chunks, dim=1)
o = None
for x, w in zip(input_shards, weight_shards):
if o is None:
o = F.conv3d(x, w, None, stride, padding, dilation, groups).float()
else:
o += F.conv3d(x, w, None, stride, padding, dilation, groups).float()
o = o.to(input.dtype)
if bias_ is not None:
o += bias_[None, :, None, None, None]
# inplace operation cannot be used during training
# output_.copy_(o)
output_list.append(o)
return torch.cat(output_list, dim=1)
class DiagonalGaussianDistribution(object):
def __init__(
self,
parameters,
deterministic=False,
):
"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
self.parameters = parameters
self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device, dtype=self.mean.dtype)
def sample(self):
# torch.randn: standard normal distribution
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device, dtype=self.mean.dtype)
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.0])
else:
if other is None: # SCH: assumes other is a standard normal distribution
return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 3, 4]).flatten(0)
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,
dim=[1, 3, 4],
).flatten(0)
def mode(self):
return self.mean
class ChannelChunkConv3d(nn.Conv3d):
CONV3D_NUMEL_LIMIT = 2**31
def _get_output_numel(self, input_shape: torch.Size) -> int:
numel = self.out_channels
if len(input_shape) == 5:
numel *= input_shape[0]
for i, d in enumerate(input_shape[-3:]):
d_out = math.floor(
(d + 2 * self.padding[i] - self.dilation[i] * (self.kernel_size[i] - 1) - 1) / self.stride[i] + 1
)
numel *= d_out
return numel
def _get_n_chunks(self, numel: int, n_channels: int):
n_chunks = math.ceil(numel / ChannelChunkConv3d.CONV3D_NUMEL_LIMIT)
n_chunks = ceil_to_divisible(n_chunks, n_channels)
return n_chunks
def forward(self, input: Tensor) -> Tensor:
if input.numel() // input.size(0) < ChannelChunkConv3d.CONV3D_NUMEL_LIMIT:
return super().forward(input)
n_in_chunks = self._get_n_chunks(input.numel(), self.in_channels)
n_out_chunks = self._get_n_chunks(self._get_output_numel(input.shape), self.out_channels)
if n_in_chunks == 1 and n_out_chunks == 1:
return super().forward(input)
outputs = []
input_shards = input.chunk(n_in_chunks, dim=1)
for weight, bias in zip(self.weight.chunk(n_out_chunks), self.bias.chunk(n_out_chunks)):
weight_shards = weight.chunk(n_in_chunks, dim=1)
o = None
for x, w in zip(input_shards, weight_shards):
if o is None:
o = F.conv3d(x, w, bias, self.stride, self.padding, self.dilation, self.groups)
else:
o += F.conv3d(x, w, None, self.stride, self.padding, self.dilation, self.groups)
outputs.append(o)
return torch.cat(outputs, dim=1)
@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
def pad_for_conv3d(x: torch.Tensor, width_pad: int, height_pad: int, time_pad: int) -> torch.Tensor:
if width_pad > 0 or height_pad > 0:
x = F.pad(x, (width_pad, width_pad, height_pad, height_pad), mode="constant", value=0)
if time_pad > 0:
x = F.pad(x, (0, 0, 0, 0, time_pad, time_pad), mode="replicate")
return x
def pad_for_conv3d_kernel_3x3x3(x: torch.Tensor) -> torch.Tensor:
n_chunks = math.ceil(x.numel() / NUMEL_LIMIT)
if n_chunks == 1:
x = F.pad(x, (1, 1, 1, 1), mode="constant", value=0)
x = F.pad(x, (0, 0, 0, 0, 1, 1), mode="replicate")
else:
out_list = []
n_chunks += 1
for inp_chunk in x.chunk(n_chunks, dim=1):
out_chunk = F.pad(inp_chunk, (1, 1, 1, 1), mode="constant", value=0)
out_chunk = F.pad(out_chunk, (0, 0, 0, 0, 1, 1), mode="replicate")
out_list.append(out_chunk)
x = torch.cat(out_list, dim=1)
return x
class PadConv3D(nn.Module):
"""
pad the first frame in temporal dimension
"""
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3):
super().__init__()
if isinstance(kernel_size, int):
kernel_size = (kernel_size,) * 3
self.kernel_size = kernel_size
# == specific padding ==
time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
assert time_kernel_size == height_kernel_size == width_kernel_size, "only support cubic kernel size"
if time_kernel_size == 3:
self.pad = pad_for_conv3d_kernel_3x3x3
else:
assert time_kernel_size == 1, f"only support kernel size 1/3 for now, got {kernel_size}"
self.pad = lambda x: x
self.conv = nn.Conv3d(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=0,
)
def forward(self, x: Tensor) -> Tensor:
x = self.pad(x)
x = self.conv(x)
return x
@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
class ChannelChunkPadConv3D(PadConv3D):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3):
super().__init__(in_channels, out_channels, kernel_size)
self.conv = ChannelChunkConv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1)
|