metadata
license: mit
Model convert from https://github.com/KichangKim/DeepDanbooru
Usage:
Basic use
import cv2
import numpy as np
import onnxruntime as rt
from huggingface_hub import hf_hub_download
tagger_model_path = hf_hub_download(repo_id="skytnt/deepdanbooru_onnx", filename="deepdanbooru.onnx")
tagger_model = rt.InferenceSession(tagger_model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
tagger_model_meta = tagger_model.get_modelmeta().custom_metadata_map
tagger_tags = eval(tagger_model_meta['tags'])
def tagger_predict(image, score_threshold):
s = 512
h, w = image.shape[:-1]
h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
ph, pw = s - h, s - w
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
image = cv2.copyMakeBorder(image, ph // 2, ph - ph // 2, pw // 2, pw - pw // 2, cv2.BORDER_REPLICATE)
image = image.astype(np.float32) / 255
image = img_new[np.newaxis, :]
probs = tagger_model.run(None, {"input_1": image})[0][0]
probs = probs.astype(np.float32)
res = []
for prob, label in zip(probs.tolist(), tagger_tags):
if prob < score_threshold:
continue
res.append(label)
return res
img = cv2.imread("test.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
tags = tagger_predict(img, 0.5)
print(tags)
Multi-gpu batch process
import cv2
import torch
import os
import numpy as np
import onnxruntime as rt
from huggingface_hub import hf_hub_download
from torch.utils.data import DataLoader, Dataset
from PIL import Image
from tqdm import tqdm
from threading import Thread
class MyDataset(Dataset):
def __init__(self, image_list):
self.image_list = image_list
def __len__(self):
length = len(self.image_list)
return length
def __getitem__(self, index):
image = Image.open(self.image_list[index]).convert("RGB")
image = np.asarray(image)
s = 512
h, w = image.shape[:-1]
h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
ph, pw = s - h, s - w
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
image = cv2.copyMakeBorder(image, ph // 2, ph - ph // 2, pw // 2, pw - pw // 2, cv2.BORDER_REPLICATE)
image = image.astype(np.float32) / 255
image = torch.from_numpy(image)
idx = torch.tensor([index], dtype=torch.int32)
return image, idx
def get_images(path):
def file_ext(fname):
return os.path.splitext(fname)[1].lower()
all_files = {
os.path.relpath(os.path.join(root, fname), path)
for root, _dirs, files in os.walk(path)
for fname in files
}
all_images = sorted(
os.path.join(path, fname) for fname in all_files if file_ext(fname) in [".png", ".jpg", ".jpeg"]
)
print(len(all_images))
return all_images
def process(all_images, batch_size=8, score_threshold=0.35):
predictions = {}
def work_fn(images, device_id):
dataset = MyDataset(images)
dataloader = DataLoader(
dataset,
batch_size=batch_size,
shuffle=False,
persistent_workers=True,
num_workers=4,
pin_memory=True,
)
for data in tqdm(dataloader):
image, idxs = data
image = image.numpy()
probs = tagger_model[device_id].run(None, {"input_1": image})[0]
probs = probs.astype(np.float32)
bs = probs.shape[0]
for i in range(bs):
tags = []
for prob, label in zip(probs[i].tolist(), tagger_tags):
if prob > score_threshold:
tags.append((label, prob))
predictions[images[idxs[i].item()]] = tags
gpu_num = len(tagger_model)
image_num = (len(all_images) // gpu_num) + 1
ts = [Thread(target=work_fn, args=(all_images[i * image_num:(i + 1) * image_num], i)) for i in range(gpu_num)]
for t in ts:
t.start()
for t in ts:
t.join()
return predictions
gpu_num = 4
batch_size = 8
tagger_model_path = hf_hub_download(repo_id="skytnt/deepdanbooru_onnx", filename="deepdanbooru.onnx")
tagger_model = [
rt.InferenceSession(tagger_model_path, providers=['CUDAExecutionProvider'], provider_options=[{'device_id': i}]) for
i in range(gpu_num)]
tagger_model_meta = tagger_model[0].get_modelmeta().custom_metadata_map
tagger_tags = eval(tagger_model_meta['tags'])
all_images = get_images("./data")
predictions = process(all_images, batch_size)