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3549cf5 | 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 | """Engineering reproduction of AlphaEarth Foundations from the paper specification."""
import math
import torch
from torch import nn
from torch.nn import functional as F
def sinusoidal_timecode(timestamps, dim, origin=None, scale=365.25 * 24 * 3600 * 1000):
if origin is None:
origin = timestamps.amin(dim=1, keepdim=True)
values = (timestamps.double() - origin.double()) / scale
values = values.float()
frequencies = torch.exp(
torch.arange(0, dim, 2, device=timestamps.device) * (-math.log(10000.0) / dim)
)
angles = values.unsqueeze(-1) * frequencies
code = torch.zeros(*timestamps.shape, dim, device=timestamps.device)
code[..., 0::2] = angles.sin()
code[..., 1::2] = angles.cos()
return code
class STPBlock(nn.Module):
"""Parallel precision, time and space operators with learned pyramid exchange."""
def __init__(self, precision_dim, time_dim, space_dim, num_heads):
super().__init__()
self.precision = nn.Sequential(
nn.GroupNorm(1, precision_dim),
nn.Conv2d(precision_dim, precision_dim, 3, padding=1),
nn.GELU(),
nn.Conv2d(precision_dim, precision_dim, 3, padding=1),
)
self.time_norm = nn.LayerNorm(time_dim)
self.time_attention = nn.MultiheadAttention(time_dim, num_heads, batch_first=True)
self.space_norm = nn.LayerNorm(space_dim)
self.space_attention = nn.MultiheadAttention(space_dim, num_heads, batch_first=True)
self.to_precision = nn.ModuleList([nn.Conv2d(time_dim, precision_dim, 1), nn.Conv2d(space_dim, precision_dim, 1)])
self.to_time = nn.Conv2d(precision_dim, time_dim, 1)
self.to_space = nn.Conv2d(precision_dim, space_dim, 1)
def forward(self, precision, time, space, frame_available):
batch, frames = precision.shape[:2]
p_size, t_size, s_size = precision.shape[-2:], time.shape[-2:], space.shape[-2:]
p = precision.flatten(0, 1)
p = p + self.precision(p)
sequence = time.permute(0, 3, 4, 1, 2).reshape(-1, frames, time.shape[2])
normalized = self.time_norm(sequence)
time_mask = (~frame_available.bool())[:, None, None, :].expand(batch, *t_size, frames).reshape(-1, frames)
sequence = sequence + self.time_attention(
normalized, normalized, normalized, key_padding_mask=time_mask, need_weights=False
)[0]
time = sequence.reshape(batch, *t_size, frames, -1).permute(0, 3, 4, 1, 2)
available = frame_available[:, :, None, None, None].to(space.dtype)
spatial = (space * available).sum(dim=1) / available.sum(dim=1).clamp_min(1)
spatial = spatial.flatten(2).transpose(1, 2)
normalized = self.space_norm(spatial)
spatial = spatial + self.space_attention(normalized, normalized, normalized, need_weights=False)[0]
spatial = spatial.transpose(1, 2).reshape(batch, -1, *s_size)
space = space + spatial[:, None]
t_flat, s_flat = time.flatten(0, 1), space.flatten(0, 1)
precision = p + self.to_precision[0](F.interpolate(t_flat, p_size, mode="bilinear", align_corners=False))
precision = precision + self.to_precision[1](F.interpolate(s_flat, p_size, mode="bilinear", align_corners=False))
time = time + self.to_time(F.interpolate(p, t_size, mode="bilinear", align_corners=False)).unflatten(0, (batch, frames))
space = space + self.to_space(F.interpolate(p, s_size, mode="bilinear", align_corners=False)).unflatten(0, (batch, frames))
return precision.unflatten(0, (batch, frames)), time, space
class ConditionalDecoder(nn.Module):
def __init__(self, embedding_dim, condition_dim, hidden_dim, output_dim):
super().__init__()
self.condition = nn.Linear(condition_dim, hidden_dim)
self.network = nn.Sequential(
nn.Conv2d(embedding_dim + hidden_dim, hidden_dim, 1),
nn.GELU(),
nn.Conv2d(hidden_dim, hidden_dim, 1),
nn.GELU(),
nn.Conv2d(hidden_dim, output_dim, 1),
)
def forward(self, embedding, condition):
context = self.condition(condition)[:, :, None, None].expand(-1, -1, *embedding.shape[-2:])
return self.network(torch.cat([embedding, context], dim=1))
class AlphaEarthFoundations(nn.Module):
def __init__(self, input_sources, target_sources, config):
super().__init__()
p_dim, t_dim, s_dim = config["precision_dim"], config["time_dim"], config["space_dim"]
self.input_names = list(input_sources)
self.target_sources = target_sources
self.embedding_dim = config["embedding_dim"]
self.vmf_kappa = float(config["vmf_kappa"])
self.projectors = nn.ModuleDict({
name: nn.Sequential(nn.Conv2d(spec["channels"], p_dim, 3, stride=2, padding=1), nn.GELU())
for name, spec in input_sources.items()
})
self.time_projector = nn.Conv2d(p_dim, t_dim, 3, stride=4, padding=1)
self.space_projector = nn.Conv2d(p_dim, s_dim, 3, stride=8, padding=1)
self.time_context = nn.Linear(t_dim, t_dim)
self.blocks = nn.ModuleList([
STPBlock(p_dim, t_dim, s_dim, config["num_heads"]) for _ in range(config["num_blocks"])
])
self.summary_query = nn.Linear(t_dim * 2, p_dim)
self.embedding_head = nn.Conv2d(p_dim, self.embedding_dim, 1)
self.embedding_upsample = nn.ConvTranspose2d(p_dim, p_dim, 4, stride=2, padding=1)
condition_dim = t_dim + config["max_geometry_dim"]
self.decoders = nn.ModuleDict({
name: ConditionalDecoder(self.embedding_dim, condition_dim, config["decoder_hidden_dim"], spec["channels"])
for name, spec in target_sources.items()
})
def _summarize(self, precision, availability, period, origin):
period_codes = sinusoidal_timecode(period, self.time_context.in_features, origin)
query = self.summary_query(period_codes.flatten(1))
scores = (precision * query[:, None, :, None, None]).sum(dim=2).mean(dim=(-1, -2))
scores = scores.masked_fill(~availability.bool(), torch.finfo(scores.dtype).min)
summary = (precision * scores.softmax(dim=1)[:, :, None, None, None]).sum(dim=1)
return F.normalize(self.embedding_head(self.embedding_upsample(summary)), dim=1)
def forward(self, sources, timestamps, valid_period, frame_available, target_times=None,
target_geometry=None, target_periods=None):
precision_parts, code_parts = [], []
origin = torch.cat(list(timestamps.values()), dim=1).amin(dim=1, keepdim=True)
for name in self.input_names:
values = sources[name]
batch, frames = values.shape[:2]
projected = self.projectors[name](values.flatten(0, 1)).unflatten(0, (batch, frames))
precision_parts.append(projected)
code_parts.append(sinusoidal_timecode(timestamps[name], self.time_context.in_features, origin))
availability = torch.cat([frame_available[name] for name in self.input_names], dim=1)
precision = torch.cat(precision_parts, dim=1)
codes = torch.cat(code_parts, dim=1)
time = self.time_projector(precision.flatten(0, 1)).unflatten(0, precision.shape[:2])
time = time + self.time_context(codes)[:, :, :, None, None]
space = self.space_projector(precision.flatten(0, 1)).unflatten(0, precision.shape[:2])
for block in self.blocks:
precision, time, space = block(precision, time, space, availability)
embedding = self._summarize(precision, availability, valid_period, origin)
outputs = {"embedding": embedding}
if target_times is not None:
outputs["reconstructions"] = {}
for name in self.target_sources:
source_embedding = self._summarize(precision, availability, target_periods[name], origin)
if self.training:
source_embedding = F.normalize(
source_embedding + torch.randn_like(source_embedding) / math.sqrt(self.vmf_kappa), dim=1
)
relative_time = (
(target_times[name] - target_periods[name][:, 0]).float()
/ (target_periods[name][:, 1] - target_periods[name][:, 0]).float().clamp_min(1)
)
time_code = sinusoidal_timecode(
relative_time[:, None], self.time_context.in_features,
torch.zeros_like(relative_time[:, None]), scale=1.0
)[:, 0]
geometry = target_geometry[name]
outputs["reconstructions"][name] = self.decoders[name](source_embedding, torch.cat([time_code, geometry], dim=1))
return outputs
def _pool_continuous(values, grid_m):
if grid_m == 10:
return values
size = max(1, round(values.shape[-1] * 10 / grid_m))
return F.adaptive_avg_pool2d(values, (size, size))
def _shift_invariant_l1(prediction, target, mask, radius):
losses = []
for dy in range(-radius, radius + 1):
for dx in range(-radius, radius + 1):
shifted = torch.roll(prediction, (dy, dx), dims=(-2, -1))
valid = mask.clone()
if dy > 0: valid[..., :dy, :] = 0
if dy < 0: valid[..., dy:, :] = 0
if dx > 0: valid[..., :, :dx] = 0
if dx < 0: valid[..., :, dx:] = 0
losses.append((torch.abs(shifted - target) * valid).sum() / valid.sum().clamp_min(1))
return torch.stack(losses).amin()
def compute_losses(teacher, student, targets, masks, text_target, target_sources, weights):
reconstruction = teacher["embedding"].new_zeros(())
components = {}
for name, spec in target_sources.items():
prediction, target, mask = teacher["reconstructions"][name], targets[name], masks[name]
grid_m = int(spec["loss_grid_m"])
if spec["type"] == "categorical":
size = max(1, round(prediction.shape[-1] * 10 / grid_m))
prediction = F.adaptive_avg_pool2d(prediction, (size, size))
one_hot = F.one_hot(target.long(), num_classes=prediction.shape[1]).permute(0, 3, 1, 2).float()
target = F.adaptive_avg_pool2d(one_hot, (size, size)).argmax(dim=1)
mask = F.adaptive_avg_pool2d(mask, (size, size))
value = F.cross_entropy(prediction, target, reduction="none")
value = (value * mask[:, 0]).sum() / mask[:, 0].sum().clamp_min(1)
else:
if spec.get("shift_pixels", 0):
value = _shift_invariant_l1(prediction, target, mask, int(spec["shift_pixels"]))
else:
prediction, target, mask = (_pool_continuous(item, grid_m) for item in (prediction, target, mask))
value = (torch.abs(prediction - target) * mask).sum() / mask.sum().clamp_min(1)
components[f"reconstruction_{name}"] = value
reconstruction = reconstruction + float(spec["weight"]) * value
flat = teacher["embedding"].permute(0, 2, 3, 1).reshape(-1, teacher["embedding"].shape[1])
rotated = torch.roll(flat, max(1, flat.shape[0] // 2), dims=0)
uniformity = (flat * rotated).sum(dim=1).abs().mean()
consistency = 1.0 - (teacher["embedding"] * student["embedding"]).sum(dim=1).mean()
pooled = F.normalize(teacher["embedding"].mean(dim=(2, 3)), dim=1)
normalized_text = F.normalize(text_target, dim=1)
logits = pooled @ normalized_text.transpose(0, 1)
labels = torch.arange(len(logits), device=logits.device)
text_alignment = 0.5 * (F.cross_entropy(logits, labels) + F.cross_entropy(logits.transpose(0, 1), labels))
total = (weights["reconstruction"] * reconstruction + weights["uniformity"] * uniformity
+ weights["consistency"] * consistency + weights["text"] * text_alignment)
components.update(reconstruction=reconstruction, uniformity=uniformity,
consistency=consistency, text_alignment=text_alignment, total=total)
return total, components
def quantize_embeddings(embedding, power=2, scale=127.5):
transformed = embedding.abs().pow(1.0 / power) * embedding.sign()
return torch.round(transformed * scale).clamp(-127, 127).to(torch.int8)
def dequantize_embeddings(quantized, power=2, scale=127.5):
values = quantized.float() / scale
return values.abs().pow(power) * values.sign()
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