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Core inference logic for S2F (Shape2Force).
Predicts force maps from bright field microscopy images.
"""
import os
import sys
import cv2
import torch
import numpy as np
# Ensure S2F is in path when running from project root or S2F
S2F_ROOT = os.path.dirname(os.path.abspath(__file__))
if S2F_ROOT not in sys.path:
sys.path.insert(0, S2F_ROOT)
from config.constants import BATCH_INFERENCE_SIZE, DEFAULT_SUBSTRATE, MODEL_INPUT_SIZE
from models.s2f_model import create_s2f_model
from utils.paths import get_ckp_base, model_subfolder
from utils.substrate_settings import get_settings_of_category, compute_settings_normalization
from utils import config
def load_image(filepath, target_size=None):
"""Load and preprocess a bright field image."""
img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
if img is None:
raise ValueError(f"Could not load image: {filepath}")
size = target_size if target_size is not None else MODEL_INPUT_SIZE
if isinstance(size, int):
size = (size, size)
img = cv2.resize(img, size)
img = img.astype(np.float32) / 255.0
return img
def sum_force_map(force_map):
"""Compute cell force as sum of pixel values scaled by SCALE_FACTOR_FORCE."""
if isinstance(force_map, np.ndarray):
force_map = torch.from_numpy(force_map.astype(np.float32))
if force_map.dim() == 2:
force_map = force_map.unsqueeze(0).unsqueeze(0) # [1, 1, H, W]
elif force_map.dim() == 3:
force_map = force_map.unsqueeze(0) # [1, 1, H, W]
# force_map: [B, 1, H, W], sum over spatial dims (2, 3)
return torch.sum(force_map, dim=(2, 3)) * config.SCALE_FACTOR_FORCE
def create_settings_channels_single(substrate_name, device, height, width, config_path=None,
substrate_config=None):
"""
Create settings channels for a single image (single-cell mode).
Args:
substrate_name: Substrate name (used if substrate_config is None)
device: torch device
height, width: spatial dimensions
config_path: Path to substrate config JSON
substrate_config: Optional dict with 'pixelsize' and 'young'. If provided, overrides substrate_name.
"""
norm_params = compute_settings_normalization(config_path=config_path)
if substrate_config is not None and 'pixelsize' in substrate_config and 'young' in substrate_config:
settings = substrate_config
else:
settings = get_settings_of_category(substrate_name, config_path=config_path)
pmin, pmax = norm_params['pixelsize']['min'], norm_params['pixelsize']['max']
ymin, ymax = norm_params['young']['min'], norm_params['young']['max']
pixelsize_norm = (settings['pixelsize'] - pmin) / (pmax - pmin) if pmax > pmin else 0.5
young_norm = (settings['young'] - ymin) / (ymax - ymin) if ymax > ymin else 0.5
pixelsize_norm = max(0.0, min(1.0, pixelsize_norm))
young_norm = max(0.0, min(1.0, young_norm))
pixelsize_ch = torch.full(
(1, 1, height, width), pixelsize_norm, device=device, dtype=torch.float32
)
young_ch = torch.full(
(1, 1, height, width), young_norm, device=device, dtype=torch.float32
)
return torch.cat([pixelsize_ch, young_ch], dim=1)
class S2FPredictor:
"""
Shape2Force predictor for single-cell or spheroid force map prediction.
"""
def __init__(self, model_type="single_cell", checkpoint_path=None, ckp_folder=None, device=None):
"""
Args:
model_type: "single_cell" or "spheroid"
checkpoint_path: Path to .pth checkpoint (relative to ckp_folder or absolute)
ckp_folder: Folder containing checkpoints (default: S2F/ckp)
device: "cuda" or "cpu" (auto-detected if None)
"""
self.model_type = model_type
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
ckp_base = get_ckp_base(S2F_ROOT)
subfolder = model_subfolder(model_type)
ckp_dir = ckp_folder if ckp_folder else os.path.join(ckp_base, subfolder)
if not os.path.isdir(ckp_dir):
ckp_dir = ckp_base # fallback if subfolders not used
in_channels = 3 if model_type == "single_cell" else 1
s2f_model_type = "s2f" if model_type == "single_cell" else "s2f_spheroid"
generator, _ = create_s2f_model(in_channels=in_channels, model_type=s2f_model_type)
self.generator = generator
if checkpoint_path:
full_path = checkpoint_path
if not os.path.isabs(checkpoint_path):
full_path = os.path.join(ckp_dir, checkpoint_path)
if not os.path.exists(full_path):
full_path = os.path.join(ckp_base, checkpoint_path) # try base folder
if not os.path.exists(full_path):
raise FileNotFoundError(f"Checkpoint not found: {full_path}")
if model_type == "single_cell":
self.generator.load_checkpoint_with_expansion(full_path, strict=True)
else:
checkpoint = torch.load(full_path, map_location="cpu", weights_only=False)
state = checkpoint.get("generator_state_dict") or checkpoint.get("model_state_dict") or checkpoint
self.generator.load_state_dict(state, strict=True)
if hasattr(self.generator, "set_output_mode"):
self.generator.set_output_mode(use_tanh=False) # sigmoid [0,1] for inference
self.generator = self.generator.to(self.device)
self.generator.eval()
self.norm_params = compute_settings_normalization() if model_type == "single_cell" else None
self._use_tanh_output = model_type == "single_cell" # single_cell uses tanh, spheroid uses sigmoid
self.config_path = os.path.join(S2F_ROOT, "config", "substrate_settings.json")
def predict(self, image_path=None, image_array=None, substrate=None,
substrate_config=None):
"""
Run prediction on an image.
Args:
image_path: Path to bright field image (tif, png, jpg)
image_array: numpy array (H, W) or (H, W, C) in [0, 255] or [0, 1]
substrate: Substrate name for single-cell mode (used if substrate_config is None)
substrate_config: Optional dict with 'pixelsize' and 'young'. Overrides substrate lookup.
Returns:
heatmap: numpy array (MODEL_INPUT_SIZE, MODEL_INPUT_SIZE) in [0, 1]
force: scalar cell force (sum of heatmap * SCALE_FACTOR_FORCE)
pixel_sum: raw sum of all pixel values in heatmap
"""
if image_path is not None:
img = load_image(image_path)
elif image_array is not None:
img = np.asarray(image_array, dtype=np.float32)
if img.ndim == 3:
img = img[:, :, 0] if img.shape[-1] >= 1 else img
if img.max() > 1.0:
img = img / 255.0
img = cv2.resize(img, (MODEL_INPUT_SIZE, MODEL_INPUT_SIZE))
else:
raise ValueError("Provide image_path or image_array")
x = torch.from_numpy(img).float().unsqueeze(0).unsqueeze(0).to(self.device) # [1,1,H,W]
if self.model_type == "single_cell" and self.norm_params is not None:
sub = substrate if substrate is not None else DEFAULT_SUBSTRATE
settings_ch = create_settings_channels_single(
sub, self.device, x.shape[2], x.shape[3],
config_path=self.config_path, substrate_config=substrate_config
)
x = torch.cat([x, settings_ch], dim=1) # [1,3,H,W]
with torch.no_grad():
pred = self.generator(x)
if self._use_tanh_output:
pred = (pred + 1.0) / 2.0 # Tanh [-1,1] to [0, 1]
# else: spheroid already outputs sigmoid [0, 1]
heatmap = pred[0, 0].cpu().numpy()
force = sum_force_map(pred).item()
pixel_sum = float(np.sum(heatmap))
return heatmap, force, pixel_sum
def predict_batch(self, images, substrate=None, substrate_config=None, batch_size=None,
on_progress=None):
"""
Run prediction on a batch of images. Processes in chunks to avoid OOM on
memory-constrained environments (e.g. Hugging Face free tier).
Args:
images: List of (img_array, key) or list of img arrays. img_array: (H, W) or (H, W, C).
substrate: Substrate name for single-cell mode (same for all images).
substrate_config: Optional dict with 'pixelsize' and 'young' (same for all).
batch_size: Max images per forward pass (default: BATCH_INFERENCE_SIZE). Use 1 for minimal memory.
on_progress: Optional callback(processed: int, total: int) called after each forward pass.
Returns:
List of (heatmap, force, pixel_sum) tuples.
"""
batch_size = batch_size if batch_size is not None else BATCH_INFERENCE_SIZE
imgs = []
for item in images:
img = item[0] if isinstance(item, tuple) else item
img = np.asarray(img, dtype=np.float32)
if img.ndim == 3:
img = img[:, :, 0] if img.shape[-1] >= 1 else img
if img.max() > 1.0:
img = img / 255.0
img = cv2.resize(img, (MODEL_INPUT_SIZE, MODEL_INPUT_SIZE))
imgs.append(img)
results = []
for start in range(0, len(imgs), batch_size):
chunk = imgs[start : start + batch_size]
x = torch.from_numpy(np.stack(chunk)).float().unsqueeze(1).to(self.device) # [B, 1, H, W]
if self.model_type == "single_cell" and self.norm_params is not None:
sub = substrate if substrate is not None else DEFAULT_SUBSTRATE
settings_ch = create_settings_channels_single(
sub, self.device, x.shape[2], x.shape[3],
config_path=self.config_path, substrate_config=substrate_config
)
settings_batch = settings_ch.expand(x.shape[0], -1, -1, -1)
x = torch.cat([x, settings_batch], dim=1) # [B, 3, H, W]
with torch.no_grad():
pred = self.generator(x)
if self._use_tanh_output:
pred = (pred + 1.0) / 2.0
for i in range(pred.shape[0]):
heatmap = pred[i, 0].cpu().numpy()
force = sum_force_map(pred[i : i + 1]).item()
pixel_sum = float(np.sum(heatmap))
results.append((heatmap, force, pixel_sum))
if on_progress is not None:
on_progress(len(results), len(imgs))
return results
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