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https://github.com/ClovertaTheTrilobita/cellpose-web.git
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85 lines
4 KiB
Python
85 lines
4 KiB
Python
import os.path
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from pathlib import Path
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from omegaconf import OmegaConf
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import redis
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import datetime
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import json
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from sympy import false
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CONFIG_PATH = Path(__file__).parent / "config.yaml"
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cfg = OmegaConf.load(CONFIG_PATH)
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cfg.data.root_dir = str((CONFIG_PATH.parent / cfg.data.root_dir).resolve())
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BASE_DIR = cfg.data.root_dir
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TRAIN_DIR = cfg.data.train.train_dir
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TEST_DIR = cfg.data.train.test_dir
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MODELS_DIR = str((CONFIG_PATH.parent / cfg.model.save_dir).resolve())
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os.environ["CELLPOSE_LOCAL_MODELS_PATH"] = MODELS_DIR
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r = redis.Redis(host="127.0.0.1", port=6379, db=0)
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def set_status(task_id, status, train_losses, test_losses, **extra):
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payload = {"status": status,
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"updated_at": datetime.datetime.utcnow().isoformat(),
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"train_losses": train_losses.tolist() if hasattr(train_losses, "tolist") else train_losses,
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"test_losses": test_losses.tolist() if hasattr(test_losses, "tolist") else test_losses,
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**extra}
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r.set(f"task:{task_id}", json.dumps(payload), ex=86400) # 1 天过期
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def get_status(task_id):
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raw = r.get(f"task:{task_id}")
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return json.loads(raw) if raw else None
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from cellpose import io, models, train
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class Cptrain:
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@classmethod
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async def start_train(cls,
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time: str | None = None,
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model_name: str | None = None,
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image_filter: str = "_img",
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mask_filter: str = "_masks",
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base_model: str = "cpsam",
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train_probs: list[float] = None,
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test_probs: list[float] = None,
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batch_size: int = 8,
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learning_rate = 5e-5,
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n_epochs: int = 100,
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weight_decay=0.1,
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normalize: bool =True,
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compute_flows: bool = False,
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min_train_masks: int = 5,
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nimg_per_epoch: int =None,
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rescale: bool= False,
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scale_range=None,
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channel_axis: int = None,
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):
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train_dir = Path(TRAIN_DIR) / time
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test_dir = Path(TEST_DIR) / time
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os.makedirs(train_dir, exist_ok=True)
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os.makedirs(test_dir, exist_ok=True)
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io.logger_setup()
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output = io.load_train_test_data(str(train_dir), str(test_dir), image_filter=image_filter,
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mask_filter=mask_filter, look_one_level_down=False)
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images, labels, image_names, test_images, test_labels, image_names_test = output
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model = models.CellposeModel(gpu=True, pretrained_model=base_model)
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set_status(time, "running", None, None)
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model_path, train_losses, test_losses = train.train_seg(model.net,
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train_data=images, train_labels=labels,
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test_data=test_images, test_labels=test_labels,
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train_probs=train_probs, test_probs=test_probs,
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weight_decay=weight_decay, learning_rate=learning_rate,
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n_epochs=n_epochs, model_name=model_name,
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save_path=BASE_DIR, batch_size=batch_size,
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normalize=normalize, compute_flows=compute_flows, min_train_masks=min_train_masks,
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nimg_per_epoch=nimg_per_epoch, rescale=rescale, scale_range=scale_range, channel_axis=channel_axis
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)
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set_status(time, "done", train_losses, test_losses)
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print("模型已保存到:", model_path)
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return train_losses, test_losses
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