File size: 4,055 Bytes
23bc32f f9f6093 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f fb56df2 23bc32f |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
class FrameEncoder(nn.Module):
def __init__(self, config: dict) -> None:
super().__init__()
assert len(config["mult"]) == len(config["down"])
encoder_layers = [nn.Conv2d(config["image_channels"], config["num_channels"], kernel_size=3, stride=1, padding=1)]
input_channels = config["num_channels"]
for m, d in zip(config["mult"], config["down"]):
output_channels = m * config["num_channels"]
encoder_layers.append(ResidualBlock(input_channels, output_channels))
input_channels = output_channels
if d:
encoder_layers.append(Downsample(output_channels))
encoder_layers.extend([
nn.GroupNorm(num_groups=32, num_channels=input_channels),
nn.SiLU(inplace=True),
nn.Conv2d(input_channels, config["latent_dim"], kernel_size=3, stride=1, padding=1)
])
self.encoder = nn.Sequential(*encoder_layers)
def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
b, t, _, _, _ = x.size()
x = rearrange(x, 'b t c h w -> (b t) c h w')
x = self.encoder(x)
x = rearrange(x, '(b t) c h w -> b t c h w', b=b, t=t)
return x
class FrameDecoder(nn.Module):
def __init__(self, config: dict) -> None:
super().__init__()
assert len(config["mult"]) == len(config["down"])
decoder_layers = []
output_channels = config["num_channels"]
for m, d in zip(config["mult"], config["down"]):
input_channels = m * config["num_channels"]
decoder_layers.append(ResidualBlock(input_channels, output_channels))
output_channels = input_channels
if d:
decoder_layers.append(Upsample(input_channels))
decoder_layers.reverse()
decoder_layers.insert(0, nn.Conv2d(config["latent_dim"], input_channels, kernel_size=3, stride=1, padding=1))
decoder_layers.extend([
nn.GroupNorm(num_groups=32, num_channels=config["num_channels"]),
nn.SiLU(inplace=True),
nn.Conv2d(config["num_channels"], config["image_channels"], kernel_size=3, stride=1, padding=1)
])
self.decoder = nn.Sequential(*decoder_layers)
def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
b, t, _, _, _ = x.size()
x = rearrange(x, 'b t c h w -> (b t) c h w')
x = self.decoder(x)
x = rearrange(x, '(b t) c h w -> b t c h w', b=b, t=t)
return x
class ResidualBlock(nn.Module):
def __init__(self, in_channels: int, out_channels: int, num_groups_norm: int = 32) -> None:
super().__init__()
self.f = nn.Sequential(
nn.GroupNorm(num_groups_norm, in_channels),
nn.SiLU(inplace=True),
nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
nn.GroupNorm(num_groups_norm, out_channels),
nn.SiLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
)
self.skip_projection = nn.Identity() if in_channels == out_channels else torch.nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.skip_projection(x) + self.f(x)
class Downsample(nn.Module):
def __init__(self, num_channels: int) -> None:
super().__init__()
self.conv = nn.Conv2d(num_channels, num_channels, kernel_size=2, stride=2, padding=0)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.conv(x)
class Upsample(nn.Module):
def __init__(self, num_channels: int) -> None:
super().__init__()
self.conv = nn.Conv2d(num_channels, num_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.interpolate(x, scale_factor=2.0, mode="nearest")
return self.conv(x)
|