Spaces:
Sleeping
Sleeping
File size: 2,268 Bytes
2c2fc49 6f2da8f 2c2fc49 |
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 |
# utils/classifier.py
from __future__ import annotations
import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image
from pathlib import Path
from utils.analysis import CLASS_NAMES
# -------------------------------------------------
# DEVICE
# -------------------------------------------------
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# -------------------------------------------------
# TRANSFORMS
# -------------------------------------------------
clf_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
])
# -------------------------------------------------
# MODEL LOADING
# -------------------------------------------------
def load_wbc_classifier(weights_path: str | Path):
"""
Load your trained ResNet50 classifier.
Expected checkpoint format:
{"model_state_dict": ..., ...}
"""
weights_path = Path(weights_path)
if not weights_path.exists():
raise FileNotFoundError(f"Classifier weights not found: {weights_path}")
# Base model (ImageNet pre-trained)
model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
# Replace FC layer with your 8-class head
model.fc = nn.Sequential(
nn.Dropout(0.3),
nn.Linear(model.fc.in_features, len(CLASS_NAMES))
)
# Load checkpoint
ckpt = torch.load(weights_path, map_location=DEVICE,weights_only=False)
if "model_state_dict" in ckpt:
model.load_state_dict(ckpt["model_state_dict"])
else:
model.load_state_dict(ckpt)
model.to(DEVICE)
model.eval()
return model
# -------------------------------------------------
# SINGLE-CROP CLASSIFICATION
# -------------------------------------------------
def classify_wbc_crop(
model: nn.Module,
pil_img: Image.Image,
) -> str:
"""
Run classification on a single crop and return predicted class name.
"""
x = clf_transform(pil_img).unsqueeze(0).to(DEVICE)
with torch.no_grad():
logits = model(x)
pred_idx = int(torch.argmax(logits, dim=1).item())
return CLASS_NAMES[pred_idx]
|