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5a5d1a8 | 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 | """Generators."""
import logging
from typing import List
import einops
import torch
import torch.nn.functional as F
from torch.nn.modules.pixelshuffle import PixelShuffle
from torch.nn.utils.parametrizations import spectral_norm
from .common import GBlock, UpsampleGBlock
from .layers import ConvGRU
logger = logging.getLogger(__name__)
logger.setLevel(logging.WARN)
class Sampler(torch.nn.Module):
"""Sampler class."""
def __init__(
self,
forecast_steps: int = 18,
latent_channels: int = 768,
context_channels: int = 384,
output_channels: int = 1,
):
"""
Sampler from the Skillful Nowcasting, see https://arxiv.org/pdf/2104.00954.pdf.
The sampler takes the output from the Latent and Context conditioning stacks and
creates one stack of ConvGRU layers per future timestep.
Args:
forecast_steps: Number of forecast steps (int)
latent_channels: Number of input channels to the lowest ConvGRU layer (int)
context_channels: Number of context channels (int)
output_channels: Number of output channels (int)
**kwargs: allow initialize of the parameters above through key pairs
"""
super().__init__()
self.forecast_steps = forecast_steps
self.convGRU1 = ConvGRU(
input_channels=latent_channels + context_channels,
output_channels=context_channels,
kernel_size=3,
)
self.gru_conv_1x1 = spectral_norm(
torch.nn.Conv2d(
in_channels=context_channels, out_channels=latent_channels, kernel_size=(1, 1)
)
)
self.g1 = GBlock(input_channels=latent_channels, output_channels=latent_channels)
self.up_g1 = UpsampleGBlock(
input_channels=latent_channels, output_channels=latent_channels // 2
)
self.convGRU2 = ConvGRU(
input_channels=latent_channels // 2 + context_channels // 2,
output_channels=context_channels // 2,
kernel_size=3,
)
self.gru_conv_1x1_2 = spectral_norm(
torch.nn.Conv2d(
in_channels=context_channels // 2,
out_channels=latent_channels // 2,
kernel_size=(1, 1),
)
)
self.g2 = GBlock(input_channels=latent_channels // 2, output_channels=latent_channels // 2)
self.up_g2 = UpsampleGBlock(
input_channels=latent_channels // 2, output_channels=latent_channels // 4
)
self.convGRU3 = ConvGRU(
input_channels=latent_channels // 4 + context_channels // 4,
output_channels=context_channels // 4,
kernel_size=3,
)
self.gru_conv_1x1_3 = spectral_norm(
torch.nn.Conv2d(
in_channels=context_channels // 4,
out_channels=latent_channels // 4,
kernel_size=(1, 1),
)
)
self.g3 = GBlock(input_channels=latent_channels // 4, output_channels=latent_channels // 4)
self.up_g3 = UpsampleGBlock(
input_channels=latent_channels // 4, output_channels=latent_channels // 8
)
self.convGRU4 = ConvGRU(
input_channels=latent_channels // 8 + context_channels // 8,
output_channels=context_channels // 8,
kernel_size=3,
)
self.gru_conv_1x1_4 = spectral_norm(
torch.nn.Conv2d(
in_channels=context_channels // 8,
out_channels=latent_channels // 8,
kernel_size=(1, 1),
)
)
self.g4 = GBlock(input_channels=latent_channels // 8, output_channels=latent_channels // 8)
self.up_g4 = UpsampleGBlock(
input_channels=latent_channels // 8, output_channels=latent_channels // 16
)
self.bn = torch.nn.BatchNorm2d(latent_channels // 16)
self.relu = torch.nn.ReLU()
self.conv_1x1 = spectral_norm(
torch.nn.Conv2d(
in_channels=latent_channels // 16,
out_channels=4 * output_channels,
kernel_size=(1, 1),
)
)
self.depth2space = PixelShuffle(upscale_factor=2)
def forward(
self, conditioning_states: List[torch.Tensor], latent_dim: torch.Tensor
) -> torch.Tensor:
"""
Perform the sampling from Skillful Nowcasting with GANs.
Args:
conditioning_states: Outputs from the `ContextConditioningStack` with the 4 input
states, ordered from largest to smallest spatially latent_dim: Output from
`LatentConditioningStack` for input into the ConvGRUs
latent_dim: (torch.Tensor)
Returns:
forecast_steps-length output of images for future timesteps
"""
# Iterate through each forecast step
# Initialize with conditioning state for first one, output for second one
init_states = conditioning_states
# Expand latent dim to match batch size
latent_dim = einops.repeat(
latent_dim, "b c h w -> (repeat b) c h w", repeat=init_states[0].shape[0]
)
hidden_states = [latent_dim] * self.forecast_steps
# Layer 4 (bottom most)
hidden_states = self.convGRU1(hidden_states, init_states[3])
hidden_states = [self.gru_conv_1x1(h) for h in hidden_states]
hidden_states = [self.g1(h) for h in hidden_states]
hidden_states = [self.up_g1(h) for h in hidden_states]
# Layer 3.
hidden_states = self.convGRU2(hidden_states, init_states[2])
hidden_states = [self.gru_conv_1x1_2(h) for h in hidden_states]
hidden_states = [self.g2(h) for h in hidden_states]
hidden_states = [self.up_g2(h) for h in hidden_states]
# Layer 2.
hidden_states = self.convGRU3(hidden_states, init_states[1])
hidden_states = [self.gru_conv_1x1_3(h) for h in hidden_states]
hidden_states = [self.g3(h) for h in hidden_states]
hidden_states = [self.up_g3(h) for h in hidden_states]
# Layer 1 (top-most).
hidden_states = self.convGRU4(hidden_states, init_states[0])
hidden_states = [self.gru_conv_1x1_4(h) for h in hidden_states]
hidden_states = [self.g4(h) for h in hidden_states]
hidden_states = [self.up_g4(h) for h in hidden_states]
# Output layer.
hidden_states = [F.relu(self.bn(h)) for h in hidden_states]
hidden_states = [self.conv_1x1(h) for h in hidden_states]
hidden_states = [self.depth2space(h) for h in hidden_states]
# Convert forecasts to a torch Tensor
forecasts = torch.stack(hidden_states, dim=1)
return forecasts
class Generator(torch.nn.Module):
"""Generator class."""
def __init__(
self,
conditioning_stack: torch.nn.Module,
latent_stack: torch.nn.Module,
sampler: torch.nn.Module,
):
"""
Wrap the three parts of the generator for simpler calling.
Args:
conditioning_stack: (torch.nn.Module)
latent_stack: (torch.nn.Module)
sampler: Combines the conditioning information and latent information (torch.nn.Module)
"""
super().__init__()
self.conditioning_stack = conditioning_stack
self.latent_stack = latent_stack
self.sampler = sampler
def forward(self, x: torch.Tensor):
"""Apply a forward pass on the tensor."""
conditioning_states = self.conditioning_stack(x)
latent_dim = self.latent_stack(x)
x = self.sampler(conditioning_states, latent_dim)
return x
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