Multihuntr

# Reimplementation of: https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/object_detection/metrics/mean_avg_precision.py
# Now with more vectorisation!

def precision_recall_curve_th(is_tp, confs, n_true, eps=1e-8):
    # Sort by confs
    order = (-confs).argsort()
    is_tp = is_tp[order]
    confs = confs[order]

    # Cumulative sum true positives and number of predictions

Multihuntr

# Utility functions for managing 3x3 matrices for cv2.warpAffine in pure numpy

import numpy as np


def identity():
    return np.eye(3, dtype=np.float64)


def affine(A=None, t=None):

Multihuntr

# I keep properties on my main nn.Modules. e.g. a list of the training statistics the model is tracking.
# I wanted to perform a set of extra actions across multiple different modules without having to
#  - write those steps into each of the 5+ different model definitions, or
#  - explicitly expose those values on the wrapper module.
# It's fairly trivial, but if you don't use the try: super(), it doesn't keep the `wrapped` property.

import torch
import torch.nn as nn

class Wrapper(nn.Module):