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35e14e9 | 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 | """SAC Patch Segmenter model for outpost deployment.
Accepts PIL images directly, runs U-Net segmentation, and returns
patch detection score + mask fraction.
Usage (inside outpost):
result = model.predict(image=pil_image)
# returns {"score": 0.85, "mask_fraction": 0.12}
Reference: Liu et al., CVPR 2022, "Segment and Complete"
"""
from __future__ import annotations
from typing import Optional
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms
from transformers import PreTrainedModel
from .configuration_sac import SACPatchSegmenterConfig
def _log(msg):
print(f"[SAC-DEBUG] {msg}", file=sys.stderr, flush=True)
# ---------------------------------------------------------------------------
# U-Net architecture (matches joellliu/SegmentAndComplete coco_at.pth)
# ---------------------------------------------------------------------------
class _DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch, mid_ch=None):
super().__init__()
mid = mid_ch or out_ch
self.double_conv = nn.Sequential(
nn.Conv2d(in_ch, mid, 3, padding=1), nn.BatchNorm2d(mid), nn.ReLU(inplace=True),
nn.Conv2d(mid, out_ch, 3, padding=1), nn.BatchNorm2d(out_ch), nn.ReLU(inplace=True),
)
def forward(self, x):
return self.double_conv(x)
class _Down(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.maxpool_conv = nn.Sequential(nn.MaxPool2d(2), _DoubleConv(in_ch, out_ch))
def forward(self, x):
return self.maxpool_conv(x)
class _Up(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
self.conv = _DoubleConv(in_ch, out_ch, in_ch // 2)
def forward(self, x1, x2):
x1 = self.up(x1)
dy, dx = x2.size(2) - x1.size(2), x2.size(3) - x1.size(3)
x1 = F.pad(x1, [dx // 2, dx - dx // 2, dy // 2, dy - dy // 2])
return self.conv(torch.cat([x2, x1], dim=1))
class _OutConv(nn.Module):
def __init__(self, in_ch, out_ch):
super().__init__()
self.conv = nn.Conv2d(in_ch, out_ch, kernel_size=1)
def forward(self, x):
return self.conv(x)
# ---------------------------------------------------------------------------
# HuggingFace PreTrainedModel wrapper
# ---------------------------------------------------------------------------
class SACPatchSegmenterModel(PreTrainedModel):
"""SAC U-Net patch segmenter with integrated preprocessing.
Accepts PIL images, resizes to 416x416, runs U-Net segmentation,
and returns patch detection results.
"""
config_class = SACPatchSegmenterConfig
supports_gradient_checkpointing = False
def __init__(self, config: SACPatchSegmenterConfig) -> None:
super().__init__(config)
bf = config.base_filter
self._input_size = config.input_size
# U-Net layers
self.inc = _DoubleConv(3, bf)
self.down1 = _Down(bf, bf * 2)
self.down2 = _Down(bf * 2, bf * 4)
self.down3 = _Down(bf * 4, bf * 8)
self.down4 = _Down(bf * 8, bf * 16 // 2)
self.up1 = _Up(bf * 16, bf * 8 // 2)
self.up2 = _Up(bf * 8, bf * 4 // 2)
self.up3 = _Up(bf * 4, bf * 2 // 2)
self.up4 = _Up(bf * 2, bf)
self.outc = _OutConv(bf, 1)
self._to_tensor = transforms.ToTensor()
def forward(self, pixel_values: Optional[torch.Tensor] = None, **kwargs):
"""Standard forward pass. Also supports predict(image=pil)."""
if "image" in kwargs:
return self.predict(**kwargs)
if pixel_values is None:
raise ValueError("Provide pixel_values tensor or image=PIL")
return self._unet_forward(pixel_values)
def _unet_forward(self, x: torch.Tensor) -> torch.Tensor:
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
return self.outc(x)
@torch.no_grad()
def predict(self, image: Image.Image, **kwargs) -> dict:
"""Accept a PIL image and return patch detection results."""
img = image.convert("RGB").resize(
(self._input_size, self._input_size), Image.Resampling.BILINEAR
)
tensor = self._to_tensor(img).unsqueeze(0).to(device=self.device, dtype=self.dtype)
logits = self._unet_forward(tensor)
prob = torch.sigmoid(logits)
mask = (prob[0, 0] > 0.5).float()
mask_fraction = float(mask.sum().item()) / mask.numel()
if mask_fraction > 0.001:
score = min(1.0, mask_fraction * 10.0)
else:
score = float(prob.max().item()) * 0.5
return {"score": score, "mask_fraction": mask_fraction}
@torch.no_grad()
def score_image(self, image: Image.Image, **kwargs) -> dict:
"""Alias for predict — matches outpost calling convention."""
return self.predict(image=image, **kwargs)
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