feature(train): 新增训练前后端（未测试）

backend/cp_train.py

@@ -1,33 +1,53 @@
 import os.path
-from cellpose import io, models, train
 from pathlib import Path
 from omegaconf import OmegaConf
+import redis
+import datetime
+import json
 
 CONFIG_PATH = Path(__file__).parent / "config.yaml"
 cfg = OmegaConf.load(CONFIG_PATH)
 cfg.data.root_dir = str((CONFIG_PATH.parent / cfg.data.root_dir).resolve())
 BASE_DIR = cfg.data.root_dir
-TEST_TRAIN_DIR = cfg.data.train.test_train_dir
+TRAIN_DIR = cfg.data.train.train_dir
-TEST_TEST_DIR = cfg.data.train.test_test_dir
+TEST_DIR = cfg.data.train.test_dir
 MODELS_DIR = str((CONFIG_PATH.parent / cfg.model.save_dir).resolve())
+os.environ["CELLPOSE_LOCAL_MODELS_PATH"] = MODELS_DIR
+
+r = redis.Redis(host="127.0.0.1", port=6379, db=0)
+
+def set_status(task_id, status, **extra):
+    payload = {"status": status, "updated_at": datetime.datetime.utcnow().isoformat(), **extra}
+    r.set(f"task:{task_id}", json.dumps(payload), ex=86400)  # 1 天过期
+
+def get_status(task_id):
+    raw = r.get(f"task:{task_id}")
+    return json.loads(raw) if raw else None
+
+from cellpose import io, models, train
 
 class Cptrain:
 
     @classmethod
-    def start_train(cls,
+    async def start_train(cls,
                     time: str | None = None,
-                    model_name: str | None = None,):
+                    model_name: str | None = None,
+                    image_filter: str = "_img",
+                    mask_filter: str = "_masks",
+                    base_model: str = "cpsam"):
 
-        train_dir = Path(TEST_TRAIN_DIR) / time
+        train_dir = Path(TRAIN_DIR) / time
-        test_dir = Path(TEST_TEST_DIR) / time
+        test_dir = Path(TEST_DIR) / time
         os.makedirs(train_dir, exist_ok=True)
         os.makedirs(test_dir, exist_ok=True)
         io.logger_setup()
-        output = io.load_train_test_data(str(train_dir), str(test_dir), image_filter="_img",
+        output = io.load_train_test_data(str(train_dir), str(test_dir), image_filter=image_filter,
-                                         mask_filter="_masks", look_one_level_down=False)
+                                         mask_filter=mask_filter, look_one_level_down=False)
         images, labels, image_names, test_images, test_labels, image_names_test = output
 
-        model = models.CellposeModel(gpu=True)
+        model = models.CellposeModel(gpu=True, pretrained_model=base_model)
+
+        set_status(time, "running")
 
         model_path, train_losses, test_losses = train.train_seg(model.net,
                                                                 train_data=images, train_labels=labels,
@@ -36,4 +56,4 @@ class Cptrain:
                                                                 n_epochs=100, model_name=model_name,
                                                                 save_path=MODELS_DIR)
 
-        print("模型已保存到:", model_path)
+        print("模型已保存到:", model_path)

backend/flaskApp.py

@@ -14,6 +14,7 @@ from flask import Flask, send_from_directory, request, jsonify
 from flask_cors import CORS
 from werkzeug.utils import secure_filename
 
+from backend.cp_train import Cptrain
 from cp_run import Cprun
 
 app = Flask(__name__)
@@ -26,6 +27,8 @@ BASE_DIR = cfg.data.root_dir
 UPLOAD_DIR = cfg.data.upload_dir
 OUTPUT_DIR = cfg.data.run.output_dir
 MODELS_DIR = str((CONFIG_PATH.parent / cfg.model.save_dir).resolve())
+TRAIN_DIR  = cfg.data.train.train_dir
+TEST_DIR = cfg.data.train.test_dir
 
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 executor = ThreadPoolExecutor(max_workers=4)
@@ -128,16 +131,50 @@ def run_upload():
 @app.post("/train_upload")
 def train_upload():
     ts = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + f"-{int(time.time()*1000)%1000:03d}"
+    model_name = request.args.get("model_name") or f"custom_model-{ts}"
+    image_filter = request.args.get("image_filter") or "_img"
+    mask_filter = request.args.get("mask_filter") or "_masks"
+    base_model = request.args.get("base_model") or "cpsam"
+
     train_files = request.files.getlist("train_files")
     test_files = request.files.getlist("test_files")
+    set_status(ts, "pending")
     saved = []
     for f in train_files:
         if not f or f.filename == "":
             continue
         name = secure_filename(f.filename)
-        f.save(os.path.join(UPLOAD_DIR, ts, name))
+        f.save(os.path.join(TRAIN_DIR, ts, name))
-        saved.append(os.path.join(UPLOAD_DIR, ts, name))
+        saved.append(os.path.join(TRAIN_DIR, ts, name))
 
+    for f in test_files:
+        if not f or f.filename == "":
+            continue
+        name = secure_filename(f.filename)
+        f.save(os.path.join(TEST_DIR, ts, name))
+        saved.append(os.path.join(TEST_DIR, ts, name))
+
+    def job():
+        return asyncio.run(Cptrain.start_train(
+            time=ts,
+            model_name=model_name,
+            image_filter=image_filter,
+            mask_filter=mask_filter,
+            base_model=base_model
+        ))
+
+    fut = executor.submit(job)
+
+    def done_cb(f):
+        try:
+            f.result()
+            set_status(ts, "success")
+        except Exception as e:
+            set_status(ts, "failed", error=str(e))
+
+    fut.add_done_callback(done_cb)
+
+    return jsonify({"ok": True, "count": len(saved), "id": ts})
 
 @app.get("/status")
 def status():

backend/train.py

@@ -0,0 +1,555 @@
+import time
+import os
+import numpy as np
+from cellpose import io, utils, models, dynamics
+from cellpose.transforms import normalize_img, random_rotate_and_resize, convert_image
+from pathlib import Path
+import torch
+from torch import nn
+from tqdm import trange
+import redis
+import json
+import datetime
+
+import logging
+
+r = redis.Redis(host="127.0.0.1", port=6379, db=0)
+
+def set_status(task_id, status, **extra):
+    payload = {"status": status, "updated_at": datetime.datetime.utcnow().isoformat(), **extra}
+    r.set(f"task:{task_id}", json.dumps(payload), ex=86400)  # 1 天过期
+
+def get_status(task_id):
+    raw = r.get(f"task:{task_id}")
+    return json.loads(raw) if raw else None
+
+train_logger = logging.getLogger(__name__)
+
+
+def _loss_fn_class(lbl, y, class_weights=None):
+    """
+    Calculates the loss function between true labels lbl and prediction y.
+
+    Args:
+        lbl (numpy.ndarray): True labels (cellprob, flowsY, flowsX).
+        y (torch.Tensor): Predicted values (flowsY, flowsX, cellprob).
+
+    Returns:
+        torch.Tensor: Loss value.
+
+    """
+
+    criterion3 = nn.CrossEntropyLoss(reduction="mean", weight=class_weights)
+    loss3 = criterion3(y[:, :-3], lbl[:, 0].long())
+
+    return loss3
+
+
+def _loss_fn_seg(lbl, y, device):
+    """
+    Calculates the loss function between true labels lbl and prediction y.
+
+    Args:
+        lbl (numpy.ndarray): True labels (cellprob, flowsY, flowsX).
+        y (torch.Tensor): Predicted values (flowsY, flowsX, cellprob).
+        device (torch.device): Device on which the tensors are located.
+
+    Returns:
+        torch.Tensor: Loss value.
+
+    """
+    criterion = nn.MSELoss(reduction="mean")
+    criterion2 = nn.BCEWithLogitsLoss(reduction="mean")
+    veci = 5. * lbl[:, -2:]
+    loss = criterion(y[:, -3:-1], veci)
+    loss /= 2.
+    loss2 = criterion2(y[:, -1], (lbl[:, -3] > 0.5).to(y.dtype))
+    loss = loss + loss2
+    return loss
+
+
+def _reshape_norm(data, channel_axis=None, normalize_params={"normalize": False}):
+    """
+    Reshapes and normalizes the input data.
+
+    Args:
+        data (list): List of input data, with channels axis first or last.
+        normalize_params (dict, optional): Dictionary of normalization parameters. Defaults to {"normalize": False}.
+
+    Returns:
+        list: List of reshaped and normalized data.
+    """
+    if (np.array([td.ndim != 3 for td in data]).sum() > 0 or
+            np.array([td.shape[0] != 3 for td in data]).sum() > 0):
+        data_new = []
+        for td in data:
+            if td.ndim == 3:
+                channel_axis0 = channel_axis if channel_axis is not None else np.array(td.shape).argmin()
+                # put channel axis first
+                td = np.moveaxis(td, channel_axis0, 0)
+                td = td[:3]  # keep at most 3 channels
+            if td.ndim == 2 or (td.ndim == 3 and td.shape[0] == 1):
+                td = np.stack((td, 0 * td, 0 * td), axis=0)
+            elif td.ndim == 3 and td.shape[0] < 3:
+                td = np.concatenate((td, 0 * td[:1]), axis=0)
+            data_new.append(td)
+        data = data_new
+    if normalize_params["normalize"]:
+        data = [
+            normalize_img(td, normalize=normalize_params, axis=0)
+            for td in data
+        ]
+    return data
+
+
+def _get_batch(inds, data=None, labels=None, files=None, labels_files=None,
+               normalize_params={"normalize": False}):
+    """
+    Get a batch of images and labels.
+
+    Args:
+        inds (list): List of indices indicating which images and labels to retrieve.
+        data (list or None): List of image data. If None, images will be loaded from files.
+        labels (list or None): List of label data. If None, labels will be loaded from files.
+        files (list or None): List of file paths for images.
+        labels_files (list or None): List of file paths for labels.
+        normalize_params (dict): Dictionary of parameters for image normalization (will be faster, if loading from files to pre-normalize).
+
+    Returns:
+        tuple: A tuple containing two lists: the batch of images and the batch of labels.
+    """
+    if data is None:
+        lbls = None
+        imgs = [io.imread(files[i]) for i in inds]
+        imgs = _reshape_norm(imgs, normalize_params=normalize_params)
+        if labels_files is not None:
+            lbls = [io.imread(labels_files[i])[1:] for i in inds]
+    else:
+        imgs = [data[i] for i in inds]
+        lbls = [labels[i][1:] for i in inds]
+    return imgs, lbls
+
+
+def _reshape_norm_save(files, channels=None, channel_axis=None,
+                       normalize_params={"normalize": False}):
+    """ not currently used -- normalization happening on each batch if not load_files """
+    files_new = []
+    for f in trange(files):
+        td = io.imread(f)
+        if channels is not None:
+            td = convert_image(td, channels=channels,
+                               channel_axis=channel_axis)
+            td = td.transpose(2, 0, 1)
+        if normalize_params["normalize"]:
+            td = normalize_img(td, normalize=normalize_params, axis=0)
+        fnew = os.path.splitext(str(f))[0] + "_cpnorm.tif"
+        io.imsave(fnew, td)
+        files_new.append(fnew)
+    return files_new
+    # else:
+    #     train_files = reshape_norm_save(train_files, channels=channels,
+    #                     channel_axis=channel_axis, normalize_params=normalize_params)
+    # elif test_files is not None:
+    #     test_files = reshape_norm_save(test_files, channels=channels,
+    #                     channel_axis=channel_axis, normalize_params=normalize_params)
+
+
+def _process_train_test(train_data=None, train_labels=None, train_files=None,
+                        train_labels_files=None, train_probs=None, test_data=None,
+                        test_labels=None, test_files=None, test_labels_files=None,
+                        test_probs=None, load_files=True, min_train_masks=5,
+                        compute_flows=False, normalize_params={"normalize": False},
+                        channel_axis=None, device=None):
+    """
+    Process train and test data.
+
+    Args:
+        train_data (list or None): List of training data arrays.
+        train_labels (list or None): List of training label arrays.
+        train_files (list or None): List of training file paths.
+        train_labels_files (list or None): List of training label file paths.
+        train_probs (ndarray or None): Array of training probabilities.
+        test_data (list or None): List of test data arrays.
+        test_labels (list or None): List of test label arrays.
+        test_files (list or None): List of test file paths.
+        test_labels_files (list or None): List of test label file paths.
+        test_probs (ndarray or None): Array of test probabilities.
+        load_files (bool): Whether to load data from files.
+        min_train_masks (int): Minimum number of masks required for training images.
+        compute_flows (bool): Whether to compute flows.
+        channels (list or None): List of channel indices to use.
+        channel_axis (int or None): Axis of channel dimension.
+        rgb (bool): Convert training/testing images to RGB.
+        normalize_params (dict): Dictionary of normalization parameters.
+        device (torch.device): Device to use for computation.
+
+    Returns:
+        tuple: A tuple containing the processed train and test data and sampling probabilities and diameters.
+    """
+    if device == None:
+        device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
+            'mps') if torch.backends.mps.is_available() else None
+
+    if train_data is not None and train_labels is not None:
+        # if data is loaded
+        nimg = len(train_data)
+        nimg_test = len(test_data) if test_data is not None else None
+    else:
+        # otherwise use files
+        nimg = len(train_files)
+        if train_labels_files is None:
+            train_labels_files = [
+                os.path.splitext(str(tf))[0] + "_flows.tif" for tf in train_files
+            ]
+            train_labels_files = [tf for tf in train_labels_files if os.path.exists(tf)]
+        if (test_data is not None or
+            test_files is not None) and test_labels_files is None:
+            test_labels_files = [
+                os.path.splitext(str(tf))[0] + "_flows.tif" for tf in test_files
+            ]
+            test_labels_files = [tf for tf in test_labels_files if os.path.exists(tf)]
+        if not load_files:
+            train_logger.info(">>> using files instead of loading dataset")
+        else:
+            # load all images
+            train_logger.info(">>> loading images and labels")
+            train_data = [io.imread(train_files[i]) for i in trange(nimg)]
+            train_labels = [io.imread(train_labels_files[i]) for i in trange(nimg)]
+        nimg_test = len(test_files) if test_files is not None else None
+        if load_files and nimg_test:
+            test_data = [io.imread(test_files[i]) for i in trange(nimg_test)]
+            test_labels = [io.imread(test_labels_files[i]) for i in trange(nimg_test)]
+
+    ### check that arrays are correct size
+    if ((train_labels is not None and nimg != len(train_labels)) or
+            (train_labels_files is not None and nimg != len(train_labels_files))):
+        error_message = "train data and labels not same length"
+        train_logger.critical(error_message)
+        raise ValueError(error_message)
+    if ((test_labels is not None and nimg_test != len(test_labels)) or
+            (test_labels_files is not None and nimg_test != len(test_labels_files))):
+        train_logger.warning("test data and labels not same length, not using")
+        test_data, test_files = None, None
+    if train_labels is not None:
+        if train_labels[0].ndim < 2 or train_data[0].ndim < 2:
+            error_message = "training data or labels are not at least two-dimensional"
+            train_logger.critical(error_message)
+            raise ValueError(error_message)
+        if train_data[0].ndim > 3:
+            error_message = "training data is more than three-dimensional (should be 2D or 3D array)"
+            train_logger.critical(error_message)
+            raise ValueError(error_message)
+
+    ### check that flows are computed
+    if train_labels is not None:
+        train_labels = dynamics.labels_to_flows(train_labels, files=train_files,
+                                                device=device)
+        if test_labels is not None:
+            test_labels = dynamics.labels_to_flows(test_labels, files=test_files,
+                                                   device=device)
+    elif compute_flows:
+        for k in trange(nimg):
+            tl = dynamics.labels_to_flows(io.imread(train_labels_files),
+                                          files=train_files, device=device)
+        if test_files is not None:
+            for k in trange(nimg_test):
+                tl = dynamics.labels_to_flows(io.imread(test_labels_files),
+                                              files=test_files, device=device)
+
+    ### compute diameters
+    nmasks = np.zeros(nimg)
+    diam_train = np.zeros(nimg)
+    train_logger.info(">>> computing diameters")
+    for k in trange(nimg):
+        tl = (train_labels[k][0]
+              if train_labels is not None else io.imread(train_labels_files[k])[0])
+        diam_train[k], dall = utils.diameters(tl)
+        nmasks[k] = len(dall)
+    diam_train[diam_train < 5] = 5.
+    if test_data is not None:
+        diam_test = np.array(
+            [utils.diameters(test_labels[k][0])[0] for k in trange(len(test_labels))])
+        diam_test[diam_test < 5] = 5.
+    elif test_labels_files is not None:
+        diam_test = np.array([
+            utils.diameters(io.imread(test_labels_files[k])[0])[0]
+            for k in trange(len(test_labels_files))
+        ])
+        diam_test[diam_test < 5] = 5.
+    else:
+        diam_test = None
+
+    ### check to remove training images with too few masks
+    if min_train_masks > 0:
+        nremove = (nmasks < min_train_masks).sum()
+        if nremove > 0:
+            train_logger.warning(
+                f"{nremove} train images with number of masks less than min_train_masks ({min_train_masks}), removing from train set"
+            )
+            ikeep = np.nonzero(nmasks >= min_train_masks)[0]
+            if train_data is not None:
+                train_data = [train_data[i] for i in ikeep]
+                train_labels = [train_labels[i] for i in ikeep]
+            if train_files is not None:
+                train_files = [train_files[i] for i in ikeep]
+            if train_labels_files is not None:
+                train_labels_files = [train_labels_files[i] for i in ikeep]
+            if train_probs is not None:
+                train_probs = train_probs[ikeep]
+            diam_train = diam_train[ikeep]
+            nimg = len(train_data)
+
+    ### normalize probabilities
+    train_probs = 1. / nimg * np.ones(nimg,
+                                      "float64") if train_probs is None else train_probs
+    train_probs /= train_probs.sum()
+    if test_files is not None or test_data is not None:
+        test_probs = 1. / nimg_test * np.ones(
+            nimg_test, "float64") if test_probs is None else test_probs
+        test_probs /= test_probs.sum()
+
+    ### reshape and normalize train / test data
+    normed = False
+    if normalize_params["normalize"]:
+        train_logger.info(f">>> normalizing {normalize_params}")
+    if train_data is not None:
+        train_data = _reshape_norm(train_data, channel_axis=channel_axis,
+                                   normalize_params=normalize_params)
+        normed = True
+    if test_data is not None:
+        test_data = _reshape_norm(test_data, channel_axis=channel_axis,
+                                  normalize_params=normalize_params)
+
+    return (train_data, train_labels, train_files, train_labels_files, train_probs,
+            diam_train, test_data, test_labels, test_files, test_labels_files,
+            test_probs, diam_test, normed)
+
+
+def train_seg(net, train_data=None, train_labels=None, train_files=None,
+              train_labels_files=None, train_probs=None, test_data=None,
+              test_labels=None, test_files=None, test_labels_files=None,
+              test_probs=None, channel_axis=None,
+              load_files=True, batch_size=1, learning_rate=5e-5, SGD=False,
+              n_epochs=100, weight_decay=0.1, normalize=True, compute_flows=False,
+              save_path=None, save_every=100, save_each=False, nimg_per_epoch=None,
+              nimg_test_per_epoch=None, rescale=False, scale_range=None, bsize=256,
+              min_train_masks=5, model_name=None, class_weights=None, ts=None):
+    """
+    Train the network with images for segmentation.
+
+    Args:
+        net (object): The network model to train.
+        train_data (List[np.ndarray], optional): List of arrays (2D or 3D) - images for training. Defaults to None.
+        train_labels (List[np.ndarray], optional): List of arrays (2D or 3D) - labels for train_data, where 0=no masks; 1,2,...=mask labels. Defaults to None.
+        train_files (List[str], optional): List of strings - file names for images in train_data (to save flows for future runs). Defaults to None.
+        train_labels_files (list or None): List of training label file paths. Defaults to None.
+        train_probs (List[float], optional): List of floats - probabilities for each image to be selected during training. Defaults to None.
+        test_data (List[np.ndarray], optional): List of arrays (2D or 3D) - images for testing. Defaults to None.
+        test_labels (List[np.ndarray], optional): List of arrays (2D or 3D) - labels for test_data, where 0=no masks; 1,2,...=mask labels. Defaults to None.
+        test_files (List[str], optional): List of strings - file names for images in test_data (to save flows for future runs). Defaults to None.
+        test_labels_files (list or None): List of test label file paths. Defaults to None.
+        test_probs (List[float], optional): List of floats - probabilities for each image to be selected during testing. Defaults to None.
+        load_files (bool, optional): Boolean - whether to load images and labels from files. Defaults to True.
+        batch_size (int, optional): Integer - number of patches to run simultaneously on the GPU. Defaults to 8.
+        learning_rate (float or List[float], optional): Float or list/np.ndarray - learning rate for training. Defaults to 0.005.
+        n_epochs (int, optional): Integer - number of times to go through the whole training set during training. Defaults to 2000.
+        weight_decay (float, optional): Float - weight decay for the optimizer. Defaults to 1e-5.
+        momentum (float, optional): Float - momentum for the optimizer. Defaults to 0.9.
+        SGD (bool, optional): Deprecated in v4.0.1+ - AdamW always used.
+        normalize (bool or dict, optional): Boolean or dictionary - whether to normalize the data. Defaults to True.
+        compute_flows (bool, optional): Boolean - whether to compute flows during training. Defaults to False.
+        save_path (str, optional): String - where to save the trained model. Defaults to None.
+        save_every (int, optional): Integer - save the network every [save_every] epochs. Defaults to 100.
+        save_each (bool, optional): Boolean - save the network to a new filename at every [save_each] epoch. Defaults to False.
+        nimg_per_epoch (int, optional): Integer - minimum number of images to train on per epoch. Defaults to None.
+        nimg_test_per_epoch (int, optional): Integer - minimum number of images to test on per epoch. Defaults to None.
+        rescale (bool, optional): Boolean - whether or not to rescale images during training. Defaults to True.
+        min_train_masks (int, optional): Integer - minimum number of masks an image must have to use in the training set. Defaults to 5.
+        model_name (str, optional): String - name of the network. Defaults to None.
+
+    Returns:
+        tuple: A tuple containing the path to the saved model weights, training losses, and test losses.
+
+    """
+    if SGD:
+        train_logger.warning("SGD is deprecated, using AdamW instead")
+
+    device = net.device
+
+    scale_range = 0.5 if scale_range is None else scale_range
+
+    if isinstance(normalize, dict):
+        normalize_params = {**models.normalize_default, **normalize}
+    elif not isinstance(normalize, bool):
+        raise ValueError("normalize parameter must be a bool or a dict")
+    else:
+        normalize_params = models.normalize_default
+        normalize_params["normalize"] = normalize
+
+    out = _process_train_test(train_data=train_data, train_labels=train_labels,
+                              train_files=train_files, train_labels_files=train_labels_files,
+                              train_probs=train_probs,
+                              test_data=test_data, test_labels=test_labels,
+                              test_files=test_files, test_labels_files=test_labels_files,
+                              test_probs=test_probs,
+                              load_files=load_files, min_train_masks=min_train_masks,
+                              compute_flows=compute_flows, channel_axis=channel_axis,
+                              normalize_params=normalize_params, device=net.device)
+    (train_data, train_labels, train_files, train_labels_files, train_probs, diam_train,
+     test_data, test_labels, test_files, test_labels_files, test_probs, diam_test,
+     normed) = out
+    # already normalized, do not normalize during training
+    if normed:
+        kwargs = {}
+    else:
+        kwargs = {"normalize_params": normalize_params, "channel_axis": channel_axis}
+
+    net.diam_labels.data = torch.Tensor([diam_train.mean()]).to(device)
+
+    if class_weights is not None and isinstance(class_weights, (list, np.ndarray, tuple)):
+        class_weights = torch.from_numpy(class_weights).to(device).float()
+        print(class_weights)
+
+    nimg = len(train_data) if train_data is not None else len(train_files)
+    nimg_test = len(test_data) if test_data is not None else None
+    nimg_test = len(test_files) if test_files is not None else nimg_test
+    nimg_per_epoch = nimg if nimg_per_epoch is None else nimg_per_epoch
+    nimg_test_per_epoch = nimg_test if nimg_test_per_epoch is None else nimg_test_per_epoch
+
+    # learning rate schedule
+    LR = np.linspace(0, learning_rate, 10)
+    LR = np.append(LR, learning_rate * np.ones(max(0, n_epochs - 10)))
+    if n_epochs > 300:
+        LR = LR[:-100]
+        for i in range(10):
+            LR = np.append(LR, LR[-1] / 2 * np.ones(10))
+    elif n_epochs > 99:
+        LR = LR[:-50]
+        for i in range(10):
+            LR = np.append(LR, LR[-1] / 2 * np.ones(5))
+
+    train_logger.info(f">>> n_epochs={n_epochs}, n_train={nimg}, n_test={nimg_test}")
+    train_logger.info(
+        f">>> AdamW, learning_rate={learning_rate:0.5f}, weight_decay={weight_decay:0.5f}"
+    )
+    optimizer = torch.optim.AdamW(net.parameters(), lr=learning_rate,
+                                  weight_decay=weight_decay)
+
+    t0 = time.time()
+    model_name = f"cellpose_{t0}" if model_name is None else model_name
+    save_path = Path.cwd() if save_path is None else Path(save_path)
+    filename = save_path / "models" / model_name
+    (save_path / "models").mkdir(exist_ok=True)
+
+    train_logger.info(f">>> saving model to {filename}")
+
+    lavg, nsum = 0, 0
+    train_losses, test_losses = np.zeros(n_epochs), np.zeros(n_epochs)
+    for iepoch in range(n_epochs):
+        np.random.seed(iepoch)
+        if nimg != nimg_per_epoch:
+            # choose random images for epoch with probability train_probs
+            rperm = np.random.choice(np.arange(0, nimg), size=(nimg_per_epoch,),
+                                     p=train_probs)
+        else:
+            # otherwise use all images
+            rperm = np.random.permutation(np.arange(0, nimg))
+        for param_group in optimizer.param_groups:
+            param_group["lr"] = LR[iepoch]  # set learning rate
+        net.train()
+        for k in range(0, nimg_per_epoch, batch_size):
+            kend = min(k + batch_size, nimg_per_epoch)
+            inds = rperm[k:kend]
+            imgs, lbls = _get_batch(inds, data=train_data, labels=train_labels,
+                                    files=train_files, labels_files=train_labels_files,
+                                    **kwargs)
+            diams = np.array([diam_train[i] for i in inds])
+            rsc = diams / net.diam_mean.item() if rescale else np.ones(
+                len(diams), "float32")
+            # augmentations
+            imgi, lbl = random_rotate_and_resize(imgs, Y=lbls, rescale=rsc,
+                                                 scale_range=scale_range,
+                                                 xy=(bsize, bsize))[:2]
+            # network and loss optimization
+            X = torch.from_numpy(imgi).to(device)
+            lbl = torch.from_numpy(lbl).to(device)
+
+            if X.dtype != net.dtype:
+                X = X.to(net.dtype)
+                lbl = lbl.to(net.dtype)
+
+            y = net(X)[0]
+            loss = _loss_fn_seg(lbl, y, device)
+            if y.shape[1] > 3:
+                loss3 = _loss_fn_class(lbl, y, class_weights=class_weights)
+                loss += loss3
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            train_loss = loss.item()
+            train_loss *= len(imgi)
+
+            # keep track of average training loss across epochs
+            lavg += train_loss
+            nsum += len(imgi)
+            # per epoch training loss
+            train_losses[iepoch] += train_loss
+        train_losses[iepoch] /= nimg_per_epoch
+
+        if iepoch == 5 or iepoch % 10 == 0:
+            lavgt = 0.
+            if test_data is not None or test_files is not None:
+                np.random.seed(42)
+                if nimg_test != nimg_test_per_epoch:
+                    rperm = np.random.choice(np.arange(0, nimg_test),
+                                             size=(nimg_test_per_epoch,), p=test_probs)
+                else:
+                    rperm = np.random.permutation(np.arange(0, nimg_test))
+                for ibatch in range(0, len(rperm), batch_size):
+                    with torch.no_grad():
+                        net.eval()
+                        inds = rperm[ibatch:ibatch + batch_size]
+                        imgs, lbls = _get_batch(inds, data=test_data,
+                                                labels=test_labels, files=test_files,
+                                                labels_files=test_labels_files,
+                                                **kwargs)
+                        diams = np.array([diam_test[i] for i in inds])
+                        rsc = diams / net.diam_mean.item() if rescale else np.ones(
+                            len(diams), "float32")
+                        imgi, lbl = random_rotate_and_resize(
+                            imgs, Y=lbls, rescale=rsc, scale_range=scale_range,
+                            xy=(bsize, bsize))[:2]
+                        X = torch.from_numpy(imgi).to(device)
+                        lbl = torch.from_numpy(lbl).to(device)
+
+                        if X.dtype != net.dtype:
+                            X = X.to(net.dtype)
+                            lbl = lbl.to(net.dtype)
+
+                        y = net(X)[0]
+                        loss = _loss_fn_seg(lbl, y, device)
+                        if y.shape[1] > 3:
+                            loss3 = _loss_fn_class(lbl, y, class_weights=class_weights)
+                            loss += loss3
+                        test_loss = loss.item()
+                        test_loss *= len(imgi)
+                        lavgt += test_loss
+                lavgt /= len(rperm)
+                test_losses[iepoch] = lavgt
+            lavg /= nsum
+            train_logger.info(
+                f"{iepoch}, train_loss={lavg:.4f}, test_loss={lavgt:.4f}, LR={LR[iepoch]:.6f}, time {time.time() - t0:.2f}s"
+            )
+            lavg, nsum = 0, 0
+
+        if iepoch == n_epochs - 1 or (iepoch % save_every == 0 and iepoch != 0):
+            if save_each and iepoch != n_epochs - 1:  # separate files as model progresses
+                filename0 = str(filename) + f"_epoch_{iepoch:04d}"
+            else:
+                filename0 = filename
+            train_logger.info(f"saving network parameters to {filename0}")
+            net.save_model(filename0)
+
+    net.save_model(filename)
+
+    return filename, train_losses, test_losses

frontend/index.html

@@ -1,4 +1,4 @@
-<!doctype html>
+<!DOCTYPE html>
 <html lang="en">
 
 <head>
@@ -10,179 +10,9 @@
 </head>
 
 <body>
-  <div style="padding: 1rem 1rem;">
-    <div class="mb-3 border" style="padding: 1rem 1rem;">
-      <div class="input-group mb-3">
-        <input id="fileInput" type="file" class="form-control" multiple />
-      </div>
 
-      <hr>
+    <a href="run.html">运行</a>
-      <div>
+    <a href="train.html">训练</a>
-        <div style="padding-right: 50rem;">
-          <div class="input-group mb-3">
-            <span class="input-group-text">flow threshold:</span>
-            <input id="flow" type="text" class="form-control" placeholder="" />
-          </div>
-
-          <div class="input-group mb-3">
-            <span class="input-group-text">cellprob threshold:</span>
-            <input id="cellprob" type="text" class="form-control" placeholder="" />
-          </div>
-
-          <div class="input-group mb-3">
-            <span class="input-group-text">diameter:</span>
-            <input id="diameter" type="text" class="form-control" placeholder="" />
-          </div>
-
-          <label>
-            <select id="model" class="form-select"></select>
-          </label>
-          </label>
-        </div>
-      </div>
-      <br>
-      <div>
-        <button id="uploadBtn" class="btn btn-success">Upload</button>
-        <progress id="bar" max="100" value="0" style="width:300px;"></progress>
-      </div>
-    </div>
-  </div>
-  <br><br><br>
-
-
-  <script src="https://cdn.jsdelivr.net/npm/axios/dist/axios.min.js"></script>
-  <script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"
-    integrity="sha384-oBqDVmMz9ATKxIep9tiCxS/Z9fNfEXiDAYTujMAeBAsjFuCZSmKbSSUnQlmh/jp3"
-    crossorigin="anonymous"></script>
-  <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha1/dist/js/bootstrap.min.js"
-    integrity="sha384-ep+dxp/oz2RKF89ALMPGc7Z89QFa32C8Uv1A3TcEK8sMzXVysblLA3+eJWTzPJzT"
-    crossorigin="anonymous"></script>
-  <script>
-    const API = "http://10.147.18.141:5000/";
-    const APT_UPLOAD = API + "run_upload";
-    const API_MODEL = API + "models";
-
-    async function loadModels() {
-      const select = document.getElementById('model');
-      // 占位 & 禁用，避免用户在加载中点选
-      select.disabled = true;
-      select.innerHTML = '<option value="">加载中…</option>';
-
-      // 可选：超时控制（5 秒）
-      const controller = new AbortController();
-      const t = setTimeout(() => controller.abort(), 5000);
-
-      try {
-        const resp = await fetch(API_MODEL, {
-          headers: { 'Accept': 'application/json' },
-          signal: controller.signal
-        });
-        if (!resp.ok) throw new Error(`HTTP ${resp.status}`);
-
-        const data = await resp.json();
-        const list = Array.isArray(data.models) ? data.models : [];
-
-        // 记住之前的选择（如果有）
-        const prev = select.value;
-
-        // 重新渲染选项
-        select.innerHTML = '<option value="" disabled selected>请选择模型</option>';
-        const seen = new Set();
-        for (const name of list) {
-          if (!name || seen.has(name)) continue;
-          seen.add(name);
-          const opt = document.createElement('option');
-          opt.value = String(name);
-          opt.textContent = String(name);
-          select.appendChild(opt);
-        }
-
-        // 如果原选择仍然存在，则恢复它
-        if (prev && seen.has(prev)) {
-          select.value = prev;
-        }
-      } catch (err) {
-        console.error('加载模型列表失败：', err);
-        select.innerHTML = '<option value="">加载失败（点击重试）</option>';
-        // 点击下拉框时重试
-        select.addEventListener('click', loadModels, { once: true });
-      } finally {
-        clearTimeout(t);
-        select.disabled = false;
-      }
-    }
-
-    document.addEventListener('DOMContentLoaded', loadModels);
-
-    function buildUrl() {
-      // 获取参数
-      const model = document.getElementById('model')?.value;
-      const flow = (document.getElementById('flow')?.value || '').trim();
-      const cellp = (document.getElementById('cellprob')?.value || '').trim();
-      const diameter = (document.getElementById('diameter')?.value || '').trim();
-
-      // 用 URLSearchParams 组装查询串
-      const qs = new URLSearchParams({
-        model: model,
-        flow_threshold: flow,
-        cellprob_threshold: cellp,
-        diameter: diameter
-      });
-
-      return `${API_UPLOAD}?${qs.toString()}`;
-    }
-
-    document.getElementById("uploadBtn").addEventListener("click", async () => {
-      const input = document.getElementById("fileInput");
-      if (!input.files.length) return alert("请选择文件");
-
-      const fd = new FormData();
-      for (const f of input.files) fd.append("files", f);
-
-      const bar = document.getElementById("bar");
-      try {
-        const URL = buildUrl();
-        const res = await axios.post(URL, fd, {
-          onUploadProgress: (e) => {
-            if (e.total) bar.value = Math.round((e.loaded * 100) / e.total);
-          },
-        });
-        // alert("上传成功：" + JSON.stringify(res.data));
-
-        // 创建一个提示元素
-        const notice = document.createElement("div");
-        notice.style.position = "fixed";
-        notice.style.top = "20px";
-        notice.style.left = "50%";
-        notice.style.transform = "translateX(-50%)";
-        notice.style.padding = "10px 20px";
-        notice.style.background = "#4caf50";
-        notice.style.color = "white";
-        notice.style.borderRadius = "8px";
-        notice.style.fontSize = "16px";
-        notice.style.zIndex = "9999";
-
-        let seconds = 3;
-        notice.textContent = `上传成功！${seconds} 秒后跳转预览页面…`;
-        document.body.appendChild(notice);
-
-        const timer = setInterval(() => {
-          seconds--;
-          if (seconds > 0) {
-            notice.textContent = `上传成功！${seconds} 秒后跳转预览页面…`;
-          } else {
-            clearInterval(timer);
-            document.body.removeChild(notice);
-            window.location.href = `preview.html?id=${encodeURIComponent(res.data['id'])}`;
-          }
-        }, 1000);
-      } catch (e) {
-        alert("上传失败：" + (e.response?.data?.message || e.message));
-      } finally {
-        bar.value = 0;
-      }
-    });
-  </script>
 </body>
 
 </html>