File size: 6,662 Bytes
c1070ce | 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 | """
VAE inference: mask (PIL) -> preprocess -> encode -> decode -> return one slice.
Input: Single grayscale mask (any size). Preprocess: Grayscale, Resize(256,256), ToTensor [0,1],
duplicate to 4 slices -> (1, 4, 256, 256) batched format.
Output: decode(z) shape (1, 4, 256, 256). We return one slice (default index 2) as PNG bytes.
"""
import io
import logging
import os
from typing import Optional, Tuple
import cv2
import numpy as np
import torch
import torch.nn as nn
import torchvision.transforms as T
from PIL import Image
from huggingface_hub import hf_hub_download
from model import VAE
logging.basicConfig(level=os.environ.get("LOG_LEVEL", "INFO").upper())
logger = logging.getLogger(__name__)
# --- Config (override via env on Hugging Face) ---
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL_REPO = os.environ.get("MODEL_REPO", "tan200224/Synthetic-CT-Scan_VAE_Conditional")
MODEL_FILENAME = os.environ.get("MODEL_FILENAME", "mask2pic_64model_47.pt")
INPUT_SIZE = 256
OUTPUT_SLICE_INDEX = 2 # Which of the 4 slices to return (0..3)
_model: Optional[nn.Module] = None
def load_model(repo_id: str = MODEL_REPO, filename: str = MODEL_FILENAME) -> nn.Module:
"""Download checkpoint from Hub and load VAE(base=64)."""
logger.info("[load_model] START repo_id=%s filename=%s device=%s", repo_id, filename, DEVICE)
path = hf_hub_download(repo_id=repo_id, filename=filename)
size_mb = os.path.getsize(path) / (1024 * 1024) if os.path.exists(path) else 0
logger.info("[load_model] checkpoint path=%s size=%.1f MB", path, size_mb)
model = VAE(base=64).to(DEVICE)
ckpt = torch.load(path, map_location=DEVICE)
if isinstance(ckpt, dict) and "model_state_dict" in ckpt:
model.load_state_dict(ckpt["model_state_dict"])
logger.info("[load_model] load_state_dict from checkpoint['model_state_dict'] OK")
else:
model.load_state_dict(ckpt)
logger.info("[load_model] load_state_dict from raw state_dict OK")
model.eval()
nparams = sum(p.numel() for p in model.parameters())
logger.info("[load_model] model.eval() set | params=%s | MODEL LOADED SUCCESSFULLY", nparams)
return model
def get_model() -> nn.Module:
"""Return cached model or load once."""
global _model
if _model is None:
logger.info("[get_model] loading model (first request)")
_model = load_model()
return _model
def preprocess_mask(mask: Image.Image, size: int = INPUT_SIZE) -> torch.Tensor:
"""
PIL mask -> (1, 4, size, size) float32 in [0, 1].
Steps: Grayscale, Resize(size, size), ToTensor, duplicate to 4 channels, add batch dim.
"""
logger.info("[preprocess] INPUT size=%s mode=%s", mask.size, mask.mode)
transform = T.Compose([
T.Grayscale(num_output_channels=1),
T.Resize((size, size), antialias=True),
T.ToTensor(),
])
x = transform(mask) # (1, H, W)
x_np = x.numpy()
logger.info("[preprocess] after Grayscale+Resize(%s)+ToTensor shape=%s min=%.4f max=%.4f mean=%.4f",
(size, size), tuple(x.shape), float(x_np.min()), float(x_np.max()), float(x_np.mean()))
x = x.repeat(4, 1, 1) # (4, H, W) - duplicate 1 channel to 4 slices
x = x.unsqueeze(0) # (1, 4, H, W) - add batch dimension
logger.info("[preprocess] after duplicate+batch shape=%s", tuple(x.shape))
return x
def mask_to_embedding(mask: Image.Image, size: int = INPUT_SIZE) -> torch.Tensor:
"""Mask -> preprocess -> encode -> z. Uses train() for forward so BatchNorm uses batch stats (batch size 1).
Returns z = mu (no sampling noise) for most faithful reconstruction."""
model = get_model()
x = preprocess_mask(mask, size=size).to(DEVICE)
model.train()
try:
with torch.no_grad():
mu, logvar = model.encode(x)
z = mu # use mean only for deterministic, most faithful reconstruction (no std*eps)
finally:
model.eval()
return z
def decode_to_slices(z: torch.Tensor) -> torch.Tensor:
"""z -> decode -> (1, 4, 256, 256) batched. Uses train() for forward so BatchNorm uses batch stats (batch size 1)."""
model = get_model()
model.train()
try:
with torch.no_grad():
out = model.decode(z)
finally:
model.eval()
out_np = out.detach().cpu().numpy()
logger.info("[model output] decode(z) shape=%s min=%.4f max=%.4f mean=%.4f",
tuple(out.shape), float(out_np.min()), float(out_np.max()), float(out_np.mean()))
return out
def enhance_slice(slice_2d: np.ndarray,
contrast: bool = True,
sharpen: bool = True) -> np.ndarray:
"""
Postprocess VAE output slice to reduce blur.
Input: float32 image [0,1]
Output: float32 image [0,1]
"""
img = slice_2d.astype(np.float32)
# --- 1. Contrast stretch (great for CT-like images) ---
if contrast:
p2, p98 = np.percentile(img, (2, 98))
if p98 > p2:
img = (img - p2) / (p98 - p2)
img = np.clip(img, 0, 1)
# --- 2. Unsharp mask (edge boost) ---
if sharpen:
blur = cv2.GaussianBlur(img, (0, 0), sigmaX=1.2)
img = cv2.addWeighted(img, 1.6, blur, -0.6, 0)
return np.clip(img, 0, 1)
def inference(mask: Image.Image, slice_index: int = OUTPUT_SLICE_INDEX) -> Tuple[np.ndarray, torch.Tensor]:
"""
Full pipeline: mask -> encode -> decode -> 4 slices.
Returns (one_slice_2d, full_output_tensor).
"""
z = mask_to_embedding(mask)
out = decode_to_slices(z) # (1, 4, 256, 256) batched
slice_idx = min(max(0, slice_index), 3)
one_slice = out[0, slice_idx].detach().cpu().numpy() # (256, 256) float [0,1]
logger.info("[output slice] slice_index=%s shape=%s min=%.4f max=%.4f",
slice_idx, one_slice.shape, float(one_slice.min()), float(one_slice.max()))
return one_slice, out
def slice_to_png(slice_2d: np.ndarray) -> bytes:
"""(H, W) float [0,1] -> clip -> scale to uint8 -> PNG bytes."""
arr = (np.clip(slice_2d, 0.0, 1.0) * 255).astype(np.uint8)
img = Image.fromarray(arr, mode="L")
buf = io.BytesIO()
img.save(buf, format="PNG")
return buf.getvalue()
def inference_to_png(mask: Image.Image,
slice_index: int = OUTPUT_SLICE_INDEX,
contrast: bool = True,
sharpen: bool = True) -> bytes:
"""Mask -> inference -> (optional) enhance -> PNG bytes."""
one_slice, _ = inference(mask, slice_index=slice_index)
one_slice = enhance_slice(one_slice, contrast=contrast, sharpen=sharpen)
return slice_to_png(one_slice)
|