Miners的作用是在训练期间,在每个batch内挖掘更难被模型区分开的样本对,这些样本对模型的性能影响最大,带来的损失更多。参照“1.1 自定义度量学习损失函数”,miner返回的是样本对的index,如[i, j],根据index可以对应到batch内embeddings构成的distance矩阵上,得到对应样本对的距离值或者相似度值,也可以理解为损失值。
5、Miner：PML 提供两种类型的挖掘函数:子集批处理 miner 及 tuple miner from pytorch_metric_learning import miners, lossesminer = miners.MultiSimilarityMiner()loss_func = losses.TripletMarginLoss()# your training loopfor i, (data, labels) in enumerate(dataloader): optimizer.zero_grad() embedding...
Mix and match losses, miners, and trainers in ways that other libraries don't allow. Installation Pip pip install pytorch-metric-learning To get the latest dev version: pip install pytorch-metric-learning==0.9.89.dev2 To install on Windows: ...
Miners: Regularizers: Samplers: Trainers: Testers: Utils: Base Classes, Mixins, and Wrappers: Overview Development Acknowledgements Contributors Algorithm implementations Example notebooks General improvements and bug fixes Facebook AI Open-source repos ...
miners test_distance_weighted_miner.py 2 changes: 1 addition & 1 deletion 2 README.md Original file line numberDiff line numberDiff line change @@ -46,7 +46,7 @@ pip install pytorch-metric-learning **To get the latest dev version**: ``` pip install pytorch-metric-learning==0.9...
from pytorch_metric_learning import miners, losses miner = miners.MultiSimilarityMiner() loss_func = losses.TripletMarginLoss() # your training loop for i, (data, labels) in enumerate(dataloader): optimizer.zero_grad() embeddings = model(data) hard_pairs = miner(embeddings, labels) loss = ...
3 src/pytorch_metric_learning/miners/__init__.py @@ -3,8 +3,7 @@ from .batch_easy_hard_miner import BatchEasyHardMiner from .batch_hard_miner import BatchHardMiner from .distance_weighted_miner import DistanceWeightedMiner from .embeddings_already_packaged_as_triplets import \ Embeddings...
import torch from pytorch_metric_learning import losses, miners from pytorch_metric_learning.utils import distributed as pml_dist import torch.distributed as dist import os import torch.multiprocessing as mp import pytorch_metric_learning.distances as distances def train(gpu): print("Init GPU", gpu...
Hi, Could you please post an example of using contrastive loss without trainers and miners, it's quite different from the contrastive loss that uses Euclidean distance between pairs? And also the reference from where the definition of this loss has been taken. Thanks...
{ PYTHON }} -m pip install . -vv"noarch:pythonrequirements:host: -numpy-pip-python >=3.6-scikit-learn-pytorch-torchvision-tqdmrun: -numpy-python >=3.6-scikit-learn-pytorch-torchvision-tqdmtest:imports: -pytorch_metric_learning-pytorch_metric_learning.losses-pytorch_metric_learning.miners-pytorch_...