File size: 3,676 Bytes
ae709c1 | 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 | """Standalone MobileNetV3-FPN string segmentation model."""
from __future__ import annotations
from pathlib import Path
from typing import Any
import torch
from torch import nn
import torch.nn.functional as F
CHECKPOINT_FORMATS = {"yoyo_string_semantic_transfer_v1", "yoyo_string_semantic_unet_v1"}
def _group_count(channels: int) -> int:
for groups in (8, 4, 2, 1):
if channels % groups == 0:
return groups
return 1
class _FPNRefine(nn.Module):
def __init__(self, channels: int):
super().__init__()
self.layers = nn.Sequential(
nn.Conv2d(channels, channels, 3, padding=1, groups=channels, bias=False),
nn.GroupNorm(_group_count(channels), channels),
nn.SiLU(inplace=True),
nn.Conv2d(channels, channels, 1, bias=False),
nn.GroupNorm(_group_count(channels), channels),
nn.SiLU(inplace=True),
)
def forward(self, value: torch.Tensor) -> torch.Tensor:
return self.layers(value)
class MobileNetV3FPNString(nn.Module):
"""MobileNetV3-Large encoder with a high-resolution FPN decoder."""
_FEATURE_INDICES = (1, 3, 6, 12, 16)
_FEATURE_CHANNELS = (16, 24, 40, 112, 960)
def __init__(self, decoder_channels: int = 32):
super().__init__()
from torchvision.models import mobilenet_v3_large
self.encoder = mobilenet_v3_large(weights=None).features
self.lateral = nn.ModuleList(
nn.Sequential(
nn.Conv2d(input_channels, decoder_channels, 1, bias=False),
nn.GroupNorm(_group_count(decoder_channels), decoder_channels),
)
for input_channels in self._FEATURE_CHANNELS
)
self.refine = nn.ModuleList(_FPNRefine(decoder_channels) for _ in range(4))
self.classifier = nn.Sequential(
_FPNRefine(decoder_channels),
nn.Conv2d(decoder_channels, 1, 1),
)
def forward(self, value: torch.Tensor) -> torch.Tensor:
input_size = value.shape[-2:]
features = []
selected = set(self._FEATURE_INDICES)
for index, layer in enumerate(self.encoder):
value = layer(value)
if index in selected:
features.append(value)
pyramid = self.lateral[-1](features[-1])
for level in range(len(features) - 2, -1, -1):
pyramid = F.interpolate(pyramid, size=features[level].shape[-2:], mode="bilinear", align_corners=False)
pyramid = self.refine[level](pyramid + self.lateral[level](features[level]))
logits = self.classifier(pyramid)
return F.interpolate(logits, size=input_size, mode="bilinear", align_corners=False)
def load_model(checkpoint: str | Path, device: str | torch.device = "cpu") -> tuple[nn.Module, dict[str, Any]]:
"""Load a StrandSeg-Lite checkpoint without importing the training repo."""
target = torch.device(device)
payload = torch.load(Path(checkpoint), map_location=target, weights_only=True)
if not isinstance(payload, dict) or payload.get("format") not in CHECKPOINT_FORMATS:
raise ValueError(f"Unsupported StrandSeg-Lite checkpoint: {checkpoint}")
config = dict(payload.get("model_config") or {})
architecture = str(config.get("architecture", "mobilenet_v3_fpn")).lower()
if architecture != "mobilenet_v3_fpn":
raise ValueError(f"This package supports mobilenet_v3_fpn, got {architecture!r}")
model = MobileNetV3FPNString(decoder_channels=max(16, int(config.get("base_channels", 16)) * 2))
model.load_state_dict(payload["state_dict"])
model.to(target).eval()
return model, payload
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