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42c70d5 | 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 | """Blue Marble model architecture (Apache-2.0)."""
import torch, torch.nn as nn, torch.nn.functional as F
S2_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12']
def conv_bn(i, o, k=3, s=1, g=1, d=1, act=True):
layers = [nn.Conv2d(i, o, k, s, d * (k // 2), dilation=d, groups=g, bias=False), nn.BatchNorm2d(o)]
if act:
layers.append(nn.ReLU6(inplace=True))
return nn.Sequential(*layers)
class SE(nn.Module):
def __init__(self, c, r=4):
super().__init__()
h = max(8, c // r)
self.f = nn.Sequential(nn.Conv2d(c, h, 1), nn.ReLU6(inplace=True), nn.Conv2d(h, c, 1), nn.Hardsigmoid())
def forward(self, x):
return x * self.f(x.mean((2, 3), keepdim=True))
class IRB(nn.Module):
def __init__(self, i, o, s=1, e=3, k=5, se=False, d=1):
super().__init__()
h = int(round(i * e))
self.res = s == 1 and i == o
self.net = nn.Sequential(conv_bn(i, h, 1) if e != 1 else nn.Identity(), conv_bn(h, h, k, s, g=h, d=d), SE(h) if se else nn.Identity(), conv_bn(h, o, 1, act=False))
def forward(self, x):
y = self.net(x)
return x + y if self.res else y
class SpectralStem(nn.Module):
def __init__(self, c, in_bands=13):
super().__init__()
self.spectral = nn.Conv2d(in_bands, c, 1, bias=False)
self.bn = nn.BatchNorm2d(c)
self.act = nn.ReLU6(inplace=True)
self.spatial = conv_bn(c, c, 3, g=c)
self.fuse = conv_bn(c, c, 1)
def forward(self, x):
return self.fuse(self.spatial(self.act(self.bn(self.spectral(x)))))
@torch.no_grad()
def slice_bands(self, idx):
new = nn.Conv2d(len(idx), self.spectral.out_channels, 1, bias=False).to(self.spectral.weight.device)
new.weight.copy_(self.spectral.weight[:, idx])
self.spectral = new
class Attn2d(nn.Module):
def __init__(self, c, heads=4, mlp=2):
super().__init__()
self.h, self.d = (heads, c // heads)
self.n1, self.q, self.k, self.v, self.o = (nn.BatchNorm2d(c), nn.Conv2d(c, c, 1), nn.Conv2d(c, c, 1), nn.Conv2d(c, c, 1), nn.Conv2d(c, c, 1))
self.mlp = nn.Sequential(nn.BatchNorm2d(c), nn.Conv2d(c, c * mlp, 1), nn.ReLU6(inplace=True), nn.Conv2d(c * mlp, c, 1))
def forward(self, x):
b, c, h, w = x.shape
n = self.n1(x)
sp = lambda t: t.reshape(b, self.h, self.d, h * w).transpose(2, 3)
q, k, v = (sp(self.q(n)), sp(self.k(n)), sp(self.v(n)))
a = (q.float() @ k.float().transpose(2, 3) / self.d ** 0.5).softmax(-1)
y = (a @ v.float()).to(x.dtype).transpose(2, 3).reshape(b, c, h, w)
x = x + self.o(y)
return x + self.mlp(x)
ARCHS = {'pico': dict(stem=8, w=(8, 16, 24, 40), d=(1, 2, 2, 2), e=2, k=3, rates=(1,), dec=16, full=False, se=False), 'nano': dict(stem=12, w=(16, 24, 48, 80), d=(1, 2, 3, 2), e=3, k=5, rates=(1, 2), dec=24, full=True, se=False), 'tiny': dict(stem=16, w=(24, 40, 80, 160), d=(2, 2, 4, 3), e=3, k=5, rates=(1, 2, 4), dec=40, full=True, se=True), 'small': dict(stem=24, w=(32, 56, 112, 208), d=(2, 3, 5, 3), e=3, k=7, rates=(1, 2, 4), dec=48, full=True, se=True), 'medium': dict(stem=32, w=(48, 96, 192, 320), d=(2, 3, 8, 4), e=4, k=7, rates=(1, 2, 4), dec=96, full=True, se=True, attn=2)}
class Encoder(nn.Module):
def __init__(self, cfg, in_bands=13):
super().__init__()
self.stem = SpectralStem(cfg['stem'], in_bands)
prev, self.stages = (cfg['stem'], nn.ModuleList())
for si, (w, n) in enumerate(zip(cfg['w'], cfg['d'])):
blocks = [IRB(prev, w, 2, cfg['e'], 3)]
blocks += [IRB(w, w, 1, cfg['e'], cfg['k'], se=cfg['se'] and si >= 2) for _ in range(n - 1)]
if si == len(cfg['w']) - 1:
blocks += [Attn2d(w, heads=8) for _ in range(cfg.get('attn', 0))]
self.stages.append(nn.Sequential(*blocks))
prev = w
self.neck = nn.ModuleList((IRB(prev, prev, 1, 2, 3, d=r) for r in cfg['rates']))
self.channels = (cfg['stem'], *cfg['w'])
def forward(self, x):
out = [self.stem(x)]
for s in self.stages:
out.append(s(out[-1]))
y = out[-1]
for n in self.neck:
y = n(y)
out[-1] = y
return out
class FPNDecoder(nn.Module):
def __init__(self, chs, dec, full=True):
super().__init__()
self.full = full
self.lat = nn.ModuleList((conv_bn(c, dec, 1) for c in chs[1:]))
self.ref = nn.ModuleList((IRB(dec, dec, 1, 2, 3) for _ in chs[1:-1]))
if full:
self.lat0 = conv_bn(chs[0], dec, 1)
self.ref0 = IRB(dec, dec, 1, 2, 3)
self.out_channels = dec
def forward(self, f):
y = self.lat[-1](f[-1])
for i in range(len(self.ref) - 1, -1, -1):
y = F.interpolate(y, scale_factor=2.0, mode='bilinear', align_corners=False) + self.lat[i](f[i + 1])
y = self.ref[i](y)
if self.full:
y = F.interpolate(y, scale_factor=2.0, mode='bilinear', align_corners=False) + self.lat0(f[0])
y = self.ref0(y)
return y
class BlueMarble(nn.Module):
def __init__(self, arch='tiny', num_classes=2, in_bands=13):
super().__init__()
cfg = ARCHS[arch]
self.arch, self.upsample = (arch, 1 if cfg['full'] else 2)
self.encoder = Encoder(cfg, in_bands)
self.decoder = FPNDecoder(self.encoder.channels, cfg['dec'], cfg['full'])
self.head = nn.Sequential(nn.Dropout2d(0.1), nn.Conv2d(cfg['dec'], num_classes, 1))
def forward(self, x):
y = self.head(self.decoder(self.encoder(x)))
if self.upsample > 1:
y = F.interpolate(y, scale_factor=float(self.upsample), mode='bilinear', align_corners=False)
return y
def fast_layout(model, in_shape, device='cuda'):
import torch.nn.functional as F
if not str(device).startswith('cuda'):
return (model, False)
try:
m = model.to(memory_format=torch.channels_last)
x = torch.randn(in_shape, device=device).to(memory_format=torch.channels_last)
with torch.autocast('cuda', dtype=torch.bfloat16):
out = m(x)
out.float().mean().backward()
m.zero_grad(set_to_none=True)
return (m, True)
except RuntimeError as e:
print('channels_last unavailable, using NCHW:', str(e)[:120], flush=True)
return (model.to(memory_format=torch.contiguous_format), False)
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