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Adapted for a self-contained package from the Apache-2.0 SatMAE++ reference
implementation at commit bf02548ab2bf5123761cf059491ac5a631fbb428.
"""
import math
import torch
from torch import nn
from torch.nn import functional as F
def sincos_1d(values, dim):
half = (dim + 1) // 2
omega = torch.exp(
-math.log(10000.0) * torch.arange(half, dtype=torch.float32)
/ max(half - 1, 1)
)
phase = values.float().unsqueeze(-1) * omega
return torch.cat((phase.sin(), phase.cos()), dim=-1)[..., :dim]
def sincos_2d(side, dim):
y, x = torch.meshgrid(
torch.arange(side, dtype=torch.float32),
torch.arange(side, dtype=torch.float32), indexing="ij"
)
split = dim // 2
return torch.cat((sincos_1d(y.flatten(), split),
sincos_1d(x.flatten(), dim - split)), dim=-1)
class TransformerBlock(nn.Module):
def __init__(self, dim, heads, mlp_ratio=4.0):
super().__init__()
self.norm1 = nn.LayerNorm(dim, eps=1e-6)
self.attn = nn.MultiheadAttention(dim, heads, batch_first=True)
self.norm2 = nn.LayerNorm(dim, eps=1e-6)
hidden = int(dim * mlp_ratio)
self.mlp = nn.Sequential(nn.Linear(dim, hidden), nn.GELU(), nn.Linear(hidden, dim))
def forward(self, x):
value = self.norm1(x)
x = x + self.attn(value, value, value, need_weights=False)[0]
return x + self.mlp(self.norm2(x))
class ChannelFirstNorm(nn.Module):
def __init__(self, channels):
super().__init__()
self.weight = nn.Parameter(torch.ones(channels))
self.bias = nn.Parameter(torch.zeros(channels))
def forward(self, x):
mean = x.mean(1, keepdim=True)
variance = (x - mean).square().mean(1, keepdim=True)
x = (x - mean) / torch.sqrt(variance + 1e-6)
return x * self.weight[:, None, None] + self.bias[:, None, None]
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
def forward(self, x):
return x + 0.5 * self.conv2(F.relu(self.conv1(x)))
class UpsampleBlock(nn.Module):
def __init__(self, hidden_channels, output_channels):
super().__init__()
self.up = nn.ConvTranspose2d(hidden_channels, hidden_channels, 4, 2, 1)
self.up_norm = ChannelFirstNorm(hidden_channels)
self.residual = ResidualBlock(hidden_channels)
self.residual_norm = ChannelFirstNorm(hidden_channels)
self.output = nn.Conv2d(hidden_channels, output_channels, 3, padding=1)
def forward(self, x):
hidden = F.leaky_relu(self.up_norm(self.up(x)))
hidden = self.residual_norm(self.residual(hidden))
return hidden, self.output(hidden)
class SatMAEPP(nn.Module):
def __init__(self, image_size=224, patch_size=16, in_channels=3,
embed_dim=1024, encoder_depth=24, encoder_heads=16,
decoder_dim=512, decoder_depth=8, decoder_heads=16,
mask_ratio=0.75, scales=None, mode="rgb", spectral_groups=None,
spatial_mask=False, norm_pix_loss=False, proj_ratio=4,
channel_embed_dim=None, decoder_channel_embed_dim=None):
super().__init__()
if image_size <= 0 or patch_size <= 0 or image_size % patch_size:
raise ValueError("image_size must be divisible by patch_size")
if in_channels <= 0 or embed_dim <= 0 or decoder_dim <= 0:
raise ValueError("channel and embedding dimensions must be positive")
if encoder_depth <= 0 or decoder_depth <= 0 or encoder_heads <= 0 or decoder_heads <= 0:
raise ValueError("transformer depths and head counts must be positive")
if embed_dim % encoder_heads or decoder_dim % decoder_heads:
raise ValueError("embedding dimensions must be divisible by head counts")
if not 0 <= mask_ratio < 1:
raise ValueError("mask_ratio must be in [0, 1)")
if mode not in {"rgb", "multispectral"}:
raise ValueError("mode must be rgb or multispectral")
self.image_size = image_size
self.patch_size = patch_size
self.in_channels = in_channels
self.mask_ratio = mask_ratio
self.mode = mode
self.spatial_mask = spatial_mask
self.norm_pix_loss = norm_pix_loss
self.grid = image_size // patch_size
self.num_patches = self.grid ** 2
self.scales = tuple(scales or ([1, 2] if mode == "rgb" else [1, 2, 4]))
expected_scales = (1, 2) if mode == "rgb" else (1, 2, 4)
if self.scales != expected_scales:
raise ValueError("supported scales are [1, 2] or [1, 2, 4]")
if mode == "rgb":
self.groups = (tuple(range(in_channels)),)
channel_embed_dim = 0
decoder_channel_embed_dim = 0
else:
groups = spectral_groups or [[0, 1, 2, 6], [3, 4, 5, 7], [8, 9]]
if sorted(channel for group in groups for channel in group) != list(range(in_channels)):
raise ValueError("spectral_groups must partition all channels")
self.groups = tuple(tuple(group) for group in groups)
channel_embed_dim = channel_embed_dim or min(256, embed_dim // 4)
decoder_channel_embed_dim = decoder_channel_embed_dim or min(128, decoder_dim // 4)
self.group_count = len(self.groups)
self.channel_embed_dim = channel_embed_dim
self.decoder_channel_embed_dim = decoder_channel_embed_dim
self.patch_embeds = nn.ModuleList([
nn.Conv2d(len(group), embed_dim, patch_size, patch_size) for group in self.groups
])
spatial_dim = embed_dim - channel_embed_dim
decoder_spatial_dim = decoder_dim - decoder_channel_embed_dim
self.register_buffer("position", sincos_2d(self.grid, spatial_dim))
self.register_buffer("decoder_position", sincos_2d(self.grid, decoder_spatial_dim))
if self.group_count > 1:
ids = torch.arange(self.group_count, dtype=torch.float32)
self.register_buffer("group_position", sincos_1d(ids, channel_embed_dim))
self.register_buffer("decoder_group_position", sincos_1d(ids, decoder_channel_embed_dim))
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.blocks = nn.ModuleList([
TransformerBlock(embed_dim, encoder_heads) for _ in range(encoder_depth)
])
self.norm = nn.LayerNorm(embed_dim, eps=1e-6)
self.decoder_embed = nn.Linear(embed_dim, decoder_dim)
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_dim))
self.decoder_blocks = nn.ModuleList([
TransformerBlock(decoder_dim, decoder_heads) for _ in range(decoder_depth)
])
self.decoder_norm = nn.LayerNorm(decoder_dim, eps=1e-6)
self.decoder_heads = nn.ModuleList([
nn.Linear(decoder_dim, len(group) * patch_size ** 2) for group in self.groups
])
hidden_channels = in_channels * proj_ratio
self.multiscale_projection = nn.Conv2d(in_channels, hidden_channels, 1)
self.multiscale_norm = ChannelFirstNorm(hidden_channels)
self.upsample_blocks = nn.ModuleList([
UpsampleBlock(hidden_channels, in_channels) for _ in self.scales[1:]
])
self.initialize_weights()
def initialize_weights(self):
nn.init.normal_(self.cls_token, std=0.02)
nn.init.normal_(self.mask_token, std=0.02)
for module in self.modules():
if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
nn.init.xavier_uniform_(module.weight.flatten(1))
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
def patchify(self, images):
batch, channels, height, width = images.shape
p = self.patch_size
if channels != self.in_channels or height != self.image_size or width != self.image_size:
raise ValueError("images must have shape [B, in_channels, image_size, image_size]")
x = images.reshape(batch, channels, height // p, p, width // p, p)
return x.permute(0, 2, 4, 1, 3, 5).reshape(batch, -1, channels * p ** 2)
def unpatchify(self, patches):
batch = patches.shape[0]
p = self.patch_size
x = patches.reshape(batch, self.grid, self.grid, self.in_channels, p, p)
return x.permute(0, 3, 1, 4, 2, 5).reshape(
batch, self.in_channels, self.image_size, self.image_size
)
def _positions(self, decoder=False):
spatial = self.decoder_position if decoder else self.position
if self.group_count == 1:
return spatial.unsqueeze(0)
group = self.decoder_group_position if decoder else self.group_position
value = torch.cat((
spatial.unsqueeze(0).expand(self.group_count, -1, -1),
group.unsqueeze(1).expand(-1, self.num_patches, -1),
), dim=-1)
return value.reshape(1, self.group_count * self.num_patches, -1)
def _mask(self, tokens, ratio):
batch, length, dim = tokens.shape
shared = self.group_count > 1 and self.spatial_mask
if shared:
kept_spatial = int(self.num_patches * (1 - ratio))
order = torch.rand(batch, self.num_patches, device=tokens.device).argsort(1)
kept = [order[:, :kept_spatial] + index * self.num_patches
for index in range(self.group_count)]
removed = [order[:, kept_spatial:] + index * self.num_patches
for index in range(self.group_count)]
shuffle = torch.cat(kept + removed, dim=1)
keep = kept_spatial * self.group_count
else:
keep = int(length * (1 - ratio))
shuffle = torch.rand(batch, length, device=tokens.device).argsort(1)
restore = shuffle.argsort(1)
visible = torch.gather(tokens, 1, shuffle[:, :keep, None].expand(-1, -1, dim))
mask = torch.ones(batch, length, device=tokens.device)
mask[:, :keep] = 0
return visible, torch.gather(mask, 1, restore), restore
def forward_encoder(self, images, ratio):
pieces = [embed(images[:, group]).flatten(2).transpose(1, 2)
for embed, group in zip(self.patch_embeds, self.groups)]
tokens = torch.cat(pieces, dim=1) + self._positions(False)
tokens, mask, restore = self._mask(tokens, ratio)
tokens = torch.cat((self.cls_token.expand(images.shape[0], -1, -1), tokens), 1)
for block in self.blocks:
tokens = block(tokens)
return self.norm(tokens), mask, restore
def forward_decoder(self, latent, restore):
tokens = self.decoder_embed(latent)
missing = restore.shape[1] + 1 - tokens.shape[1]
restored = torch.cat((tokens[:, 1:], self.mask_token.expand(tokens.shape[0], missing, -1)), 1)
restored = torch.gather(restored, 1, restore[:, :, None].expand(-1, -1, tokens.shape[-1]))
tokens = torch.cat((tokens[:, :1], restored + self._positions(True)), 1)
for block in self.decoder_blocks:
tokens = block(tokens)
decoded = self.decoder_norm(tokens)[:, 1:].reshape(
tokens.shape[0], self.group_count, self.num_patches, -1
)
group_predictions = [head(decoded[:, index]) for index, head in enumerate(self.decoder_heads)]
patch_channels = []
for prediction, group in zip(group_predictions, self.groups):
patch_channels.append(prediction.reshape(
prediction.shape[0], self.num_patches, len(group), self.patch_size ** 2
))
patches = torch.empty(
tokens.shape[0], self.num_patches, self.in_channels, self.patch_size ** 2,
device=tokens.device, dtype=group_predictions[0].dtype
)
for values, group in zip(patch_channels, self.groups):
patches[:, :, list(group)] = values
return patches.flatten(2), group_predictions
def forward_multiscale(self, reconstruction):
hidden = self.multiscale_norm(F.gelu(self.multiscale_projection(reconstruction)))
predictions = {"1": reconstruction}
for scale, block in zip(self.scales[1:], self.upsample_blocks):
hidden, predictions[str(scale)] = block(hidden)
return predictions
def forward(self, images, high_resolution_targets=None, mask_ratio=None):
ratio = self.mask_ratio if mask_ratio is None else mask_ratio
if images.ndim != 4 or images.shape[1:] != (self.in_channels, self.image_size, self.image_size):
raise ValueError("images must have shape [B, in_channels, image_size, image_size]")
if not 0 <= ratio < 1:
raise ValueError("mask_ratio must be in [0, 1)")
high_resolution_targets = high_resolution_targets or {}
latent, mask, restore = self.forward_encoder(images, ratio)
patch_prediction, group_predictions = self.forward_decoder(latent, restore)
target_patches = self.patchify(images)
loss_target = target_patches
if self.norm_pix_loss:
mean = loss_target.mean(-1, keepdim=True)
variance = loss_target.var(-1, keepdim=True, unbiased=False)
loss_target = (loss_target - mean) / torch.sqrt(variance + 1e-6)
grouped_mask = mask.reshape(mask.shape[0], self.group_count, self.num_patches)
prediction_channels = patch_prediction.reshape(
patch_prediction.shape[0], self.num_patches,
self.in_channels, self.patch_size ** 2
)
target_channels = loss_target.reshape(
loss_target.shape[0], self.num_patches,
self.in_channels, self.patch_size ** 2
)
base_total = mask.new_zeros(())
removed = mask.new_zeros(())
for index, group in enumerate(self.groups):
group_error = (
prediction_channels[:, :, list(group)]
- target_channels[:, :, list(group)]
).square().mean(dim=(-1, -2))
base_total = base_total + (group_error * grouped_mask[:, index]).sum()
removed = removed + grouped_mask[:, index].sum()
base_mse = base_total / removed.clamp_min(1)
base_l1_total = mask.new_zeros(())
for index, group in enumerate(self.groups):
group_l1 = (prediction_channels[:, :, list(group)] -
target_channels[:, :, list(group)]).abs().mean(dim=(-1, -2))
base_l1_total = base_l1_total + (group_l1 * grouped_mask[:, index]).sum()
base_l1 = base_l1_total / removed.clamp_min(1)
base_loss = base_mse + base_l1
reconstruction = self.unpatchify(patch_prediction)
predictions = self.forward_multiscale(reconstruction)
targets = {"1": images}
multiscale_losses = {}
for scale in self.scales[1:]:
key = str(scale)
target = None if high_resolution_targets is None else high_resolution_targets.get(key)
if target is None:
raise ValueError(f"native target for scale x{scale} is required")
expected_shape = (images.shape[0], self.in_channels,
self.image_size * scale, self.image_size * scale)
if tuple(target.shape) != expected_shape:
raise ValueError(f"target x{scale} must have shape {expected_shape}")
targets[key] = target
multiscale_losses[key] = F.mse_loss(predictions[key], target) + F.l1_loss(
predictions[key], target
)
multiscale_loss = (sum(multiscale_losses.values()) / len(multiscale_losses)
if multiscale_losses else base_loss.new_zeros(()))
return {
"loss": base_loss + multiscale_loss,
"reconstruction_loss": base_loss,
"multiscale_loss": multiscale_loss,
"reconstruction": reconstruction,
"patch_prediction": patch_prediction,
"target_patches": loss_target,
"predictions": predictions,
"targets": targets,
"mask": mask.bool(),
"features": latent,
"ids_restore": restore,
"group_predictions": group_predictions,
"scale_losses": multiscale_losses,
}
def satmaepp_vit_base(**kwargs):
return SatMAEPP(embed_dim=768, encoder_depth=12, encoder_heads=12,
decoder_dim=512, decoder_depth=8, decoder_heads=16, **kwargs)
def satmaepp_vit_large(**kwargs):
return SatMAEPP(embed_dim=1024, encoder_depth=24, encoder_heads=16,
decoder_dim=512, decoder_depth=8, decoder_heads=16, **kwargs)
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