ModelCheckpoint是一个回调函数,用于在训练过程中保存模型的权重。当设置save_best_only=True时,它将只保存在验证集上性能最好的模型权重。 ModelCheckpoint的主要参数包括: filepath:保存模型权重的路径和文件名格式。 monitor:要监测的指标,如'val_loss'或'val_accuracy'。
Here is the checkpoint hook from the config file: default_hooks = dict( checkpoint=dict(interval=1, max_keep_ckpts=1, save_best='auto'), logger=dict(type='LoggerHook', interval=5)) The checkpoint best_coco_bbox_mAP_epoch_1.pth (692.3MB) is much smaller than epoch_50.pth (2.1GB)...
default_hooks.checkpoint.save_best='coco/AP' This works normally in non-quantized training. However when inheriting the_base_in the QAT config,mmrazor.engine.runner.quantization_loops.QATValLoopcallsafter_val_epochhook twice with different keys as seenhere ...
使用 model.fit()或 model.fit_generator() 在一个大型数据集上启动数十轮的训练，有点类似于扔一架...
if not os.path.exists(task): flgo.gen_task(gen_config, task_path=task) # running fedavg on the specified task #在option中加上save_checkpoint即可自动生成和保存checkpoint,若需要从指定checkpoint中恢复,则加上load_checkpoint关键字即可 # checkpoint名称可由用户任意定义,且同一checkpoint可以被多个算法或...
trained_checkpoint_prefix='/home/tiwang/code/jupyter-notebook/DIEN/dien/dnn_best_model/ckpt_noshuffDIEN3'export_dir='/home/tiwang/code/jupyter-notebook/DIEN/dien/saved_model'graph=tf.Graph() config= tf.compat.v1.ConfigProto(allow_soft_placement=True, log_device_placement=True) ...
def save_checkpoint( checkpoint: Dict, logdir: Union[Path, str], suffix: str, is_best: bool = False, is_last: bool = False, special_suffix: str = "", ): """@TODO: Docs. Contribution is welcome.""" os.makedirs(logdir, exist_ok=True) filename = f"{logdir}/{suffix}.pth" torc...
def convert_model(): trained_checkpoint_prefix = '/home/tiwang/code/jupyter-notebook/DIEN/dien/dnn_best_model/ckpt_noshuffDIEN3' export_dir = '/home/tiwang/code/jupyter-notebook/DIEN/dien/saved_model' graph = tf.Graph() config = tf.compat.v1.ConfigProto(allow_soft_placement=True, log...
python tools/export_model.py -c configs/det/ch_PP-OCRv3/ch_PP-OCRv3_det_student.yml -o Global.pretrained_model=./output/ch_PP-OCR_V3_det/best_model/model Global.save_inference_dir=./output/ch_PP-OCR_v3_det_inferer 使用的配置文件见附件。
instances of a device with a few thousand registers in it. The result was aconfigfile that was about 5 gigabytes in size, enough to overload mosttext editors. But Simics was able to parse and load thischeckpoint filewithout a hitch. It is nice when your product surprises you in this ...