import torch.multiprocessing as multiprocessing import torchvision.transforms as transforms from torch import autocast from torch.utils.data import Dataset, DataLoader from PIL import Image import torch from torchvision.transforms import InterpolationMode from tqdm import tqdm import json import os torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.autograd.set_detect_anomaly(False) torch.autograd.profiler.emit_nvtx(enabled=False) torch.autograd.profiler.profile(enabled=False) torch.backends.cudnn.benchmark = True class ImageDataset(Dataset): def __init__(self, image_folder_path, allowed_extensions): self.allowed_extensions = allowed_extensions self.all_image_paths, self.all_image_names, self.image_base_paths = self.get_image_paths(image_folder_path) self.train_size = len(self.all_image_paths) print(f"Number of images to be tagged: {self.train_size}") self.thin_transform = transforms.Compose([ transforms.Resize(224, interpolation=InterpolationMode.BICUBIC), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize(mean=[ 0.48145466, 0.4578275, 0.40821073 ], std=[ 0.26862954, 0.26130258, 0.27577711 ]) # Normalize image ]) self.normal_transform = transforms.Compose([ transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize(mean=[ 0.48145466, 0.4578275, 0.40821073 ], std=[ 0.26862954, 0.26130258, 0.27577711 ]) # Normalize image ]) def get_image_paths(self, folder_path): image_paths = [] image_file_names = [] image_base_paths = [] for root, dirs, files in os.walk(folder_path): for file in files: if file.lower().split(".")[-1] in self.allowed_extensions: image_paths.append((os.path.abspath(os.path.join(root, file)))) image_file_names.append(file.split(".")[0]) image_base_paths.append(root) return image_paths, image_file_names, image_base_paths def __len__(self): return len(self.all_image_paths) def __getitem__(self, index): image = Image.open(self.all_image_paths[index]).convert("RGB") ratio = image.height / image.width if ratio > 2.0 or ratio < 0.5: image = self.thin_transform(image) else: image = self.normal_transform(image) return { 'image': image, "image_name": self.all_image_names[index], "image_root": self.image_base_paths[index] } def prepare_model(model_path: str): model = torch.load(model_path) model.to(memory_format=torch.channels_last) model = model.eval() return model def train(tagging_is_running, model, dataloader, train_data, output_queue): print('Begin tagging') model.eval() counter = 0 with torch.no_grad(): for i, data in tqdm(enumerate(dataloader), total=int(len(train_data) / dataloader.batch_size)): this_data = data['image'].to("cuda") with autocast(device_type='cuda', dtype=torch.bfloat16): outputs = model(this_data) probabilities = torch.nn.functional.sigmoid(outputs) output_queue.put((probabilities.to("cpu"), data["image_name"], data["image_root"])) counter += 1 _ = tagging_is_running.get() print("Tagging finished!") def tag_writer(tagging_is_running, output_queue, threshold): with open("tags.json", "r") as file: tags = json.load(file) allowed_tags = sorted(tags) del tags allowed_tags.extend(["placeholder0", "placeholder1", "placeholder2"]) tag_count = len(allowed_tags) assert tag_count == 7704, f"The length of loss scaling factor is not correct. Correct: 7704, current: {tag_count}" while not (tagging_is_running.qsize() > 0 and output_queue.qsize() > 0): tag_probabilities, image_names, image_roots = output_queue.get() tag_probabilities = tag_probabilities.tolist() for per_image_tag_probabilities, image_name, image_root in zip(tag_probabilities, image_names, image_roots, strict=True): this_image_tags = [] this_image_tag_probabilities = [] for index, per_tag_probability in enumerate(per_image_tag_probabilities): if per_tag_probability > threshold: tag = allowed_tags[index] if "placeholder" not in tag: this_image_tags.append(tag) this_image_tag_probabilities.append(str(int(round(per_tag_probability, 3) * 1000))) output_file = os.path.join(image_root, os.path.splitext(image_name)[0] + ".txt") with open(output_file, "w", encoding="utf-8") as this_output: this_output.write(" ".join(this_image_tags)) this_output.write("\n") this_output.write(" ".join(this_image_tag_probabilities)) def main(): image_folder_path = "/path/to/your/folder/" # all images should be in this folder and/or its subfolders. # I will generate a text file for every image. model_path = "/path/to/your/model.pth" allowed_extensions = {"jpg", "jpeg", "png", "webp"} batch_size = 64 # if you have a 24GB card, you can try 256 threshold = 0.3 multiprocessing.set_start_method('spawn') output_queue = multiprocessing.Queue() tagging_is_running = multiprocessing.Queue(maxsize=5) tagging_is_running.put("Running!") if not torch.cuda.is_available(): raise RuntimeError("CUDA is not available!") model = prepare_model(model_path).to("cuda") dataset = ImageDataset(image_folder_path, allowed_extensions) batched_loader = DataLoader( dataset, batch_size=batch_size, shuffle=False, num_workers=6, # if you have a big batch size, a good cpu, and enough cpu memory, try 12 pin_memory=True, drop_last=False, ) process_writer = multiprocessing.Process(target=tag_writer, args=(tagging_is_running, output_queue, threshold)) process_writer.start() process_tagger = multiprocessing.Process(target=train, args=(tagging_is_running, model, batched_loader, dataset, output_queue,)) process_tagger.start() process_writer.join() process_tagger.join() if __name__ == "__main__": main()