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from __future__ import annotations
from pathlib import Path
from typing import Dict
import numpy as np
import pandas as pd
from PIL import Image
import torch
from torchvision import transforms
import timm
from timm.models.vision_transformer import resize_pos_embed
import joblib
# ----------------------- paths & device -----------------------
ROOT_DIR = Path(__file__).resolve().parent.parent # AMLGroupSpaceFinal/
BASELINE_DIR = ROOT_DIR / "baseline"
LIST_DIR = ROOT_DIR / "list"
PLANT_CKPT_PATH = BASELINE_DIR / "plant_dinov2_patch14.pth"
LOGREG_PATH = BASELINE_DIR / "logreg_baseline.joblib"
SCALER_PATH = BASELINE_DIR / "scaler_baseline.joblib"
SPECIES_LIST_PATH = LIST_DIR / "species_list.txt"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# ----------------------- helpers (trimmed from evaluate.py) -----------------------
def read_species(p: Path):
"""Read species_list.txt and return list of species names in index order."""
rows = []
with open(p, "r", encoding="utf-8") as f:
for ln in f:
ln = ln.strip()
if not ln or ln.startswith("#"):
continue
if ";" in ln:
cid, name = ln.split(";", 1)
else:
parts = ln.split()
cid, name = parts[0], " ".join(parts[1:]) if len(parts) > 1 else ""
try:
cid = int(cid)
except ValueError:
continue
rows.append((cid, name))
df = pd.DataFrame(rows, columns=["class_id", "species_name"])
# same order as in training: iterrows order
names = list(df["species_name"])
return names
def pool_feats(out):
feats = out
if isinstance(out, dict):
for key in ("pooled", "x_norm_clstoken", "cls_token", "x"):
if key in out:
feats = out[key]
break
if isinstance(feats, (list, tuple)):
feats = feats[0]
if isinstance(feats, torch.Tensor) and feats.dim() == 3:
feats = feats[:, 0] if feats.size(1) > 1 else feats.mean(dim=1)
if isinstance(feats, torch.Tensor) and feats.dim() > 2:
feats = feats.flatten(1)
return feats
def _unwrap_state_dict(obj):
if isinstance(obj, dict):
for k in ("state_dict", "model", "module", "ema", "shadow",
"backbone", "net", "student", "teacher"):
if k in obj and isinstance(obj[k], dict):
return obj[k]
return obj
def _strip_prefixes(sd, prefixes=("module.", "backbone.", "model.", "student.")):
out = {}
for k, v in sd.items():
for p in prefixes:
if k.startswith(p):
k = k[len(p):]
out[k] = v
return out
def maybe_load_plant_ckpt(model, ckpt_path: Path):
if not ckpt_path.is_file():
print(f"[baseline] plant ckpt not found at {ckpt_path}, using generic DINOv2 weights.")
return
try:
sd = torch.load(ckpt_path, map_location="cpu")
sd = _unwrap_state_dict(sd)
sd = _strip_prefixes(sd)
msd = model.state_dict()
if "pos_embed" in sd and "pos_embed" in msd and sd["pos_embed"].shape != msd["pos_embed"].shape:
sd["pos_embed"] = resize_pos_embed(sd["pos_embed"], msd["pos_embed"])
print(f"[baseline] interpolated pos_embed to {tuple(msd['pos_embed'].shape)}")
missing, unexpected = model.load_state_dict(sd, strict=False)
print(f"[baseline] loaded plant ckpt; missing={len(missing)} unexpected={len(unexpected)}")
except Exception as e:
print(f"[baseline] failed to load '{ckpt_path}': {e}")
def build_backbone(size: int = 224):
model = timm.create_model(
"vit_base_patch14_dinov2",
pretrained=True, # generic DINOv2 as fallback
num_classes=0, # features only
img_size=size,
pretrained_cfg_overlay=dict(input_size=(3, size, size)),
).to(DEVICE)
pe = getattr(model, "patch_embed", None)
if pe is not None:
if hasattr(pe, "img_size"):
pe.img_size = (size, size)
if hasattr(pe, "strict_img_size"):
pe.strict_img_size = False
maybe_load_plant_ckpt(model, PLANT_CKPT_PATH)
model.eval()
for p in model.parameters():
p.requires_grad = False
return model
# ----------------------- global objects (loaded once) -----------------------
IMAGE_SIZE = 224
species_names = read_species(SPECIES_LIST_PATH)
num_classes = len(species_names)
backbone = build_backbone(IMAGE_SIZE)
transform = transforms.Compose([
transforms.Resize(int(IMAGE_SIZE * 1.12)),
transforms.CenterCrop(IMAGE_SIZE),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]),
])
scaler = joblib.load(SCALER_PATH)
logreg = joblib.load(LOGREG_PATH)
# ----------------------- public API for Gradio -----------------------
def predict_baseline(image: Image.Image, top_k: int = 5) -> Dict[str, float]:
"""
Run DINOv2 + Logistic Regression baseline on a single PIL image.
Returns {class_name: probability} for the top_k classes.
"""
if image is None:
return {}
x = transform(image).unsqueeze(0).to(DEVICE)
with torch.no_grad():
out = backbone.forward_features(x)
feats = pool_feats(out).cpu().numpy()
feats_scaled = scaler.transform(feats)
probs = logreg.predict_proba(feats_scaled)[0] # shape [num_classes]
top_idx = np.argsort(-probs)[:top_k]
result = {species_names[i]: float(probs[i]) for i in top_idx}
return result
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