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936b68d | 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 | """ORMBG (Open Remove Background Model, an IS-Net from xuebinqin/DIS) in MLX: a port of schirrmacher/ormbg's
`ormbg/models/ormbg.py` (Apache-2.0).
import mlx_model
model = mlx_model.load("fp16") # a folder with config.json + model.safetensors
alpha = mlx_model.matte(model, rgb) # rgb: HxWx3 uint8 -> HxW uint8 alpha (255 = foreground)
Runs on its own next to its weights (mlx and numpy). NHWC; each BatchNorm is folded into the conv before it (exact
in fp32, done at conversion), so the weights are conv kernels and biases only.
"""
from __future__ import annotations
import json
from pathlib import Path
import mlx.core as mx
import mlx.nn as nn
import numpy as np
CONFIG = {"model_type": "ormbg", "input_size": 1024}
PRECISIONS = ["bf16", "fp16"] # all convolutions: MLX can't quantize them (model.toml)
def resize_bilinear(x, size):
"""torch F.interpolate(mode="bilinear", align_corners=False) for NHWC."""
b, h, w, c = x.shape
oh, ow = size
if (oh, ow) == (h, w):
return x
def axis(n_in, n_out):
s = (np.arange(n_out, dtype=np.float64) + 0.5) * (n_in / n_out) - 0.5
s = np.clip(s, 0, None)
i0 = np.minimum(np.floor(s).astype(np.int32), n_in - 1)
i1 = np.minimum(i0 + 1, n_in - 1)
f = (s - i0).astype(np.float32)
return mx.array(i0), mx.array(i1), mx.array(f)
y0, y1, fy = axis(h, oh)
x0, x1, fx = axis(w, ow)
fy = fy.astype(x.dtype)[None, :, None, None]
fx = fx.astype(x.dtype)[None, None, :, None]
top, bot = x[:, y0], x[:, y1]
top = top[:, :, x0] * (1 - fx) + top[:, :, x1] * fx
bot = bot[:, :, x0] * (1 - fx) + bot[:, :, x1] * fx
return top * (1 - fy) + bot * fy
def maxpool2(x):
"""MaxPool2d(2, 2, ceil_mode=True), NHWC."""
b, h, w, c = x.shape
ph, pw = h % 2, w % 2
if ph or pw:
x = mx.pad(x, [(0, 0), (0, ph), (0, pw), (0, 0)], constant_values=-float("inf"))
h, w = h + ph, w + pw
return x.reshape(b, h // 2, 2, w // 2, 2, c).max(axis=(2, 4))
class REBNCONV(nn.Module):
def __init__(self, cin, cout, d=1):
super().__init__()
self.conv_s1 = nn.Conv2d(cin, cout, 3, padding=d, dilation=d)
def __call__(self, x):
return nn.relu(self.conv_s1(x))
class RSU(nn.Module):
"""RSU-L (L = depth: 7, 6, 5, 4): an encoder-decoder with pooling; dilation 2 at the bottom."""
def __init__(self, depth, cin, mid, cout):
super().__init__()
self.depth = depth
self.rebnconvin = REBNCONV(cin, cout)
setattr(self, "rebnconv1", REBNCONV(cout, mid))
for i in range(2, depth):
setattr(self, f"rebnconv{i}", REBNCONV(mid, mid))
setattr(self, f"rebnconv{depth}", REBNCONV(mid, mid, d=2))
for i in range(depth - 1, 1, -1):
setattr(self, f"rebnconv{i}d", REBNCONV(mid * 2, mid))
self.rebnconv1d = REBNCONV(mid * 2, cout)
def __call__(self, x):
hxin = self.rebnconvin(x)
enc, hx = [], hxin
for i in range(1, self.depth):
hx = getattr(self, f"rebnconv{i}")(hx)
enc.append(hx)
if i < self.depth - 1:
hx = maxpool2(hx)
hx = getattr(self, f"rebnconv{self.depth}")(hx)
for i in range(self.depth - 1, 0, -1):
hx = getattr(self, f"rebnconv{i}d")(mx.concatenate([hx, enc[i - 1]], -1))
if i > 1:
hx = resize_bilinear(hx, enc[i - 2].shape[1:3])
return hx + hxin
class RSU4F(nn.Module):
"""RSU-4F: no pooling, dilations 1, 2, 4, 8."""
def __init__(self, cin, mid, cout):
super().__init__()
self.rebnconvin = REBNCONV(cin, cout)
self.rebnconv1 = REBNCONV(cout, mid)
self.rebnconv2 = REBNCONV(mid, mid, 2)
self.rebnconv3 = REBNCONV(mid, mid, 4)
self.rebnconv4 = REBNCONV(mid, mid, 8)
self.rebnconv3d = REBNCONV(mid * 2, mid, 4)
self.rebnconv2d = REBNCONV(mid * 2, mid, 2)
self.rebnconv1d = REBNCONV(mid * 2, cout)
def __call__(self, x):
hxin = self.rebnconvin(x)
hx1 = self.rebnconv1(hxin)
hx2 = self.rebnconv2(hx1)
hx3 = self.rebnconv3(hx2)
hx4 = self.rebnconv4(hx3)
hx3d = self.rebnconv3d(mx.concatenate([hx4, hx3], -1))
hx2d = self.rebnconv2d(mx.concatenate([hx3d, hx2], -1))
return self.rebnconv1d(mx.concatenate([hx2d, hx1], -1)) + hxin
class Model(nn.Module):
def __init__(self, config: dict = CONFIG):
super().__init__()
self.size = config["input_size"]
self.conv_in = nn.Conv2d(3, 64, 3, stride=2, padding=1)
self.stage1, self.stage2 = RSU(7, 64, 32, 64), RSU(6, 64, 32, 128)
self.stage3, self.stage4 = RSU(5, 128, 64, 256), RSU(4, 256, 128, 512)
self.stage5, self.stage6 = RSU4F(512, 256, 512), RSU4F(512, 256, 512)
self.stage5d, self.stage4d = RSU4F(1024, 256, 512), RSU(4, 1024, 128, 256)
self.stage3d, self.stage2d, self.stage1d = RSU(5, 512, 64, 128), RSU(6, 256, 32, 64), RSU(7, 128, 16, 64)
self.side1 = nn.Conv2d(64, 1, 3, padding=1)
def __call__(self, x):
"""x: (B, S, S, 3) in [0, 1] -> (B, S, S, 1) foreground probability (side output 1, the one used)."""
hxin = self.conv_in(x)
hx1 = self.stage1(hxin)
hx2 = self.stage2(maxpool2(hx1))
hx3 = self.stage3(maxpool2(hx2))
hx4 = self.stage4(maxpool2(hx3))
hx5 = self.stage5(maxpool2(hx4))
hx6 = self.stage6(maxpool2(hx5))
up = lambda a, b: resize_bilinear(a, b.shape[1:3])
hx5d = self.stage5d(mx.concatenate([up(hx6, hx5), hx5], -1))
hx4d = self.stage4d(mx.concatenate([up(hx5d, hx4), hx4], -1))
hx3d = self.stage3d(mx.concatenate([up(hx4d, hx3), hx3], -1))
hx2d = self.stage2d(mx.concatenate([up(hx3d, hx2), hx2], -1))
hx1d = self.stage1d(mx.concatenate([up(hx2d, hx1), hx1], -1))
return mx.sigmoid(resize_bilinear(self.side1(hx1d), x.shape[1:3]))
def load(folder) -> Model:
folder = Path(folder)
model = Model(json.loads((folder / "config.json").read_text()))
model.load_weights(str(folder / "model.safetensors"), strict=True)
mx.eval(model.parameters())
return model
def predict(model: Model, rgb: np.ndarray) -> np.ndarray:
"""The raw model output at the original size, float32 HxW in [0, 1] (as ormbg/inference.py, before min-max)."""
dt = model.conv_in.weight.dtype
h, w, _ = rgb.shape
x = mx.array(rgb.astype(np.float32))[None]
x = mx.floor(resize_bilinear(x, (model.size, model.size))) / 255.0 # the original rounds to uint8 first
y = model(x.astype(dt)).astype(mx.float32)
return np.array(resize_bilinear(y, (h, w)))[0, :, :, 0]
def matte(model: Model, rgb: np.ndarray) -> np.ndarray:
"""HxWx3 uint8 -> HxW uint8 alpha, min-max normalized like ormbg/inference.py."""
p = predict(model, rgb)
p = (p - p.min()) / max(p.max() - p.min(), 1e-8)
return (p * 255).astype(np.uint8)
def convert(source: Path) -> dict:
"""runs/source/models/ormbg.pth -> conv weights with each BatchNorm folded in (fp32), MLX layout."""
import torch
sd = {k: v.double() for k, v in torch.load(Path(source) / "models" / "ormbg.pth", map_location="cpu",
weights_only=True).items()}
out = {}
for k, w in sd.items():
if not k.endswith(".weight") or w.ndim != 4:
continue
base = k[:-len(".weight")]
b = sd.get(base + ".bias", torch.zeros(w.shape[0], dtype=torch.float64))
bn = base.replace("conv_s1", "bn_s1")
if bn != base and bn + ".running_mean" in sd:
g = sd[bn + ".weight"] / torch.sqrt(sd[bn + ".running_var"] + 1e-5)
w = w * g[:, None, None, None]
b = (b - sd[bn + ".running_mean"]) * g + sd[bn + ".bias"]
if base.startswith("side") and base != "side1":
continue # side outputs 2-6 only matter for training
out[base + ".weight"] = w.float().numpy().transpose(0, 2, 3, 1)
out[base + ".bias"] = b.float().numpy()
return out
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