Spaces:
Sleeping
Sleeping
File size: 6,235 Bytes
b2ffc9b a730958 b2ffc9b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author : Romain Graux
@date : 2023 March 17, 10:56:06
@last modified : 2023 July 18, 10:25:32
"""
# Naive import of atomdetection, maybe should make a package out of it
from functools import lru_cache
import sys
from .tiff_utils import tiff_to_png
if ".." not in sys.path:
sys.path.append("..")
import os
import torch
import numpy as np
from PIL import Image
from PIL.Image import Image as PILImage
from typing import Union
from utils.constants import ModelArgs
from utils.paths import MODELS_PATH, DATASET_PATH
from atoms_detection.dl_detection import DLDetection
from atoms_detection.evaluation import Evaluation
LOGS_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "logs")
VOID_DS = os.path.join(DATASET_PATH, "void.csv")
DET_PATH = os.path.join(LOGS_PATH, "detections")
INF_PATH = os.path.join(LOGS_PATH, "inference_cache")
from atoms_detection.create_crop_dataset import create_crop
from atoms_detection.vae_svi_train import SVItrainer, init_dataloader
from atoms_detection.vae_model import rVAE
from sklearn.mixture import GaussianMixture
@lru_cache(maxsize=100)
def get_vae_model(
in_dim: tuple = (21, 21),
latent_dim: int = 50,
coord: int = 3,
seed: int = 42,
):
return rVAE(in_dim=in_dim, latent_dim=latent_dim, coord=coord, seed=seed)
def multimers_classification(
img,
coords,
likelihood,
n_species,
latent_dim: int = 50,
coord: int = 3,
reg_covar: float = 0.0001,
seed: int = 42,
epochs: int = 20,
scale_factor: float = 3.0,
batch_size: int = 100,
):
def get_crops(img, coords):
"""Get crops from image and coords"""
crops = np.array(
[np.array(create_crop(Image.fromarray(img), x, y)) for x, y in coords]
) # TODO : can be optimized if computationally heavy (multithreading)
return crops
# Get crops to train VAE on
crops = get_crops(img, coords)
# Initialize VAE
rvae = rVAE(in_dim=(21, 21), latent_dim=latent_dim, coord=coord, seed=seed)
# Train VAE to reconstruct crops
torch_crops = torch.tensor(crops).float()
train_loader = init_dataloader(torch_crops, batch_size=batch_size)
trainer = SVItrainer(rvae)
for e in range(epochs):
trainer.step(train_loader, scale_factor=scale_factor)
trainer.print_statistics()
# Extract latent space (only mean) from VAE
z_mean, _ = rvae.encode(torch_crops)
# Cluster latent space with GMM
gmm = GaussianMixture(
n_components=n_species, reg_covar=reg_covar, random_state=seed
)
preds = gmm.fit_predict(z_mean)
pred_proba = gmm.predict_proba(z_mean)
pred_proba = np.array([pred_proba[i, pred] for i, pred in enumerate(preds)])
# To order clusters, signal-to-noise ratio OR median (across crops) of some intensity quality (eg mean top-5% int)
cluster_median_values = list()
for k in range(n_species):
relevant_crops = crops[preds == k]
crop_95_percentile = np.percentile(relevant_crops, q=95, axis=0)
img_means = []
for crop, q in zip(relevant_crops, crop_95_percentile):
if (crop >= q).any():
img_means.append(crop.mean())
cluster_median_value = np.median(np.array(img_means))
cluster_median_values.append(cluster_median_value)
# Sort clusters by median value
sorted_clusters = sorted(
[(mval, c_id) for c_id, mval in enumerate(cluster_median_values)]
)
# Return results in a dict with cluster id as key
results = {}
for _, c_id in sorted_clusters:
c_idd = np.array([c_id])
results[c_id] = {
"coords": coords[preds == c_idd],
"likelihood": likelihood[preds == c_idd],
"confidence": pred_proba[preds == c_idd],
}
return results
def inference_fn(
architecture: ModelArgs,
image: Union[str, PILImage],
threshold: float,
n_species: int,
):
if architecture != ModelArgs.BASICCNN:
raise ValueError(f"Architecture {architecture} not supported yet")
ckpt_filename = os.path.join(
MODELS_PATH,
{
ModelArgs.BASICCNN: "model_C_NT_CLIP.ckpt",
# ModelArgs.BASICCNN: "model_replicate20.ckpt",
# ModelArgs.RESNET18 "inference_resnet.ckpt",
}[architecture],
)
detection = DLDetection(
model_name=architecture,
ckpt_filename=ckpt_filename,
dataset_csv=VOID_DS,
threshold=threshold,
detections_path=DET_PATH,
inference_cache_path=INF_PATH,
batch_size=512,
)
# Force the image to be in float32 because otherwise it will output wrong results (probably due to the median filter)
if type(image) == str:
image = Image.open(image)
img = np.asarray(image, dtype=np.float32)
# if img.max() <= 1:
# raise ValueError("Gradio seems to preprocess badly the tiff images. Did you adapt the preprocessing function as mentionned in the app.py file comments?")
prepro_img, _, pred_map = detection.image_to_pred_map(img, return_intermediate=True)
center_coords_list, likelihood_list = (np.array(x) for x in detection.pred_map_to_atoms(pred_map))
results = (
multimers_classification(
img=prepro_img,
coords=center_coords_list,
likelihood=likelihood_list,
n_species=n_species,
)
if n_species > 1
else {
0: {
"coords": center_coords_list,
"likelihood": likelihood_list,
"confidence": np.ones(len(center_coords_list)),
}
}
)
for k, v in results.items():
results[k]["atoms_bbs"] = [
Evaluation.center_coords_to_bbox(center_coords)
for center_coords in v["coords"]
]
return tiff_to_png(image), {
"image": tiff_to_png(image),
"pred_map": pred_map,
"species": results,
}
if __name__ == "__main__":
from utils.paths import IMG_PATH
img_path = os.path.join(IMG_PATH, "091_HAADF_15nm_Sample_PtNC_21Oct20.tif")
_ = inference_fn(ModelArgs.BASICCNN, Image.open(img_path), 0.8)
|