Common Functions¶

LOGGER¶

This is the logger that is used everywhere in this library.

from pytorch_metric_learning.utils import common_functions as c_f
c_f.LOGGER.info("Using the PML logger")

LOGGER_NAME¶

The default logger name is "PML". You can set the logging level for just this library:

import logging
from pytorch_metric_learning.utils import common_functions as c_f
logging.basicConfig()
logging.getLogger(c_f.LOGGER_NAME).setLevel(logging.INFO)

set_logger_name¶

Allows you to change LOGGER_NAME

from pytorch_metric_learning.utils import common_functions as c_f
c_f.set_logger_name("DOGS")
c_f.LOGGER.info("Hello") # prints INFO:DOGS:Hello

COLLECT_STATS¶

Default value is False. This is used by all distances, losses, miners, reducers, and regularizers. Set this to True if you want to turn on all statistics collection.

from pytorch_metric_learning.utils import common_functions as c_f
c_f.COLLECT_STATS = True

NUMPY_RANDOM¶

Default value is np.random. This is used anytime a numpy random function is needed. You can set it to something else if you want

import numpy as np
from pytorch_metric_learning.utils import common_functions as c_f
c_f.NUMPY_RANDOM = np.random.RandomState(42)

TorchInitWrapper¶

A simpler wrapper to convert the torch weight initialization functions into class form, which can then be applied within loss functions.

Example usage:

from pytorch_metric_learning.utils import common_functions as c_f
import torch

# use kaiming_uniform, with a=1 and mode='fan_out'
weight_init_func = c_f.TorchInitWrapper(torch.nn.kaiming_uniform_, a=1, mode='fan_out')
loss_func = SomeClassificationLoss(..., weight_init_func=weight_init_func)