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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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"""Model validation metrics.""" |
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import math |
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import warnings |
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from pathlib import Path |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import torch |
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from ultralytics.utils import LOGGER, SimpleClass, TryExcept, plt_settings |
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OKS_SIGMA = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0 |
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def bbox_ioa(box1, box2, iou=False, eps=1e-7): |
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""" |
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Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format. |
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Args: |
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box1 (np.array): A numpy array of shape (n, 4) representing n bounding boxes. |
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box2 (np.array): A numpy array of shape (m, 4) representing m bounding boxes. |
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iou (bool): Calculate the standard iou if True else return inter_area/box2_area. |
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eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. |
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Returns: |
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(np.array): A numpy array of shape (n, m) representing the intersection over box2 area. |
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""" |
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# Get the coordinates of bounding boxes |
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b1_x1, b1_y1, b1_x2, b1_y2 = box1.T |
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b2_x1, b2_y1, b2_x2, b2_y2 = box2.T |
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# Intersection area |
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inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \ |
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(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0) |
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# Box2 area |
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area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) |
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if iou: |
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box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1) |
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area = area + box1_area[:, None] - inter_area |
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# Intersection over box2 area |
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return inter_area / (area + eps) |
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def box_iou(box1, box2, eps=1e-7): |
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""" |
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Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. |
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Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py |
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Args: |
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box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes. |
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box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes. |
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eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. |
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Returns: |
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(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2. |
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""" |
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# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) |
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(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) |
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inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2) |
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# IoU = inter / (area1 + area2 - inter) |
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return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) |
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def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): |
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""" |
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Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4). |
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Args: |
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box1 (torch.Tensor): A tensor representing a single bounding box with shape (1, 4). |
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box2 (torch.Tensor): A tensor representing n bounding boxes with shape (n, 4). |
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xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in |
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(x1, y1, x2, y2) format. Defaults to True. |
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GIoU (bool, optional): If True, calculate Generalized IoU. Defaults to False. |
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DIoU (bool, optional): If True, calculate Distance IoU. Defaults to False. |
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CIoU (bool, optional): If True, calculate Complete IoU. Defaults to False. |
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eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. |
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Returns: |
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(torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags. |
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""" |
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# Get the coordinates of bounding boxes |
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if xywh: # transform from xywh to xyxy |
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(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) |
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w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 |
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b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ |
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b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ |
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else: # x1, y1, x2, y2 = box1 |
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b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) |
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b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) |
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w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps |
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w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps |
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# Intersection area |
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inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp_(0) * \ |
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(b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp_(0) |
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# Union Area |
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union = w1 * h1 + w2 * h2 - inter + eps |
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# IoU |
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iou = inter / union |
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if CIoU or DIoU or GIoU: |
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cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width |
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ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height |
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if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 |
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c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared |
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rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 |
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if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 |
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v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) |
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with torch.no_grad(): |
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alpha = v / (v - iou + (1 + eps)) |
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return iou - (rho2 / c2 + v * alpha) # CIoU |
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return iou - rho2 / c2 # DIoU |
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c_area = cw * ch + eps # convex area |
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return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf |
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return iou # IoU |
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def mask_iou(mask1, mask2, eps=1e-7): |
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""" |
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Calculate masks IoU. |
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Args: |
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mask1 (torch.Tensor): A tensor of shape (N, n) where N is the number of ground truth objects and n is the |
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product of image width and height. |
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mask2 (torch.Tensor): A tensor of shape (M, n) where M is the number of predicted objects and n is the |
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product of image width and height. |
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eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. |
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Returns: |
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(torch.Tensor): A tensor of shape (N, M) representing masks IoU. |
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""" |
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intersection = torch.matmul(mask1, mask2.T).clamp_(0) |
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union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection |
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return intersection / (union + eps) |
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def kpt_iou(kpt1, kpt2, area, sigma, eps=1e-7): |
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""" |
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Calculate Object Keypoint Similarity (OKS). |
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Args: |
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kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints. |
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kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints. |
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area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth. |
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sigma (list): A list containing 17 values representing keypoint scales. |
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eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. |
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Returns: |
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(torch.Tensor): A tensor of shape (N, M) representing keypoint similarities. |
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""" |
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d = (kpt1[:, None, :, 0] - kpt2[..., 0]) ** 2 + (kpt1[:, None, :, 1] - kpt2[..., 1]) ** 2 # (N, M, 17) |
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sigma = torch.tensor(sigma, device=kpt1.device, dtype=kpt1.dtype) # (17, ) |
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kpt_mask = kpt1[..., 2] != 0 # (N, 17) |
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e = d / (2 * sigma) ** 2 / (area[:, None, None] + eps) / 2 # from cocoeval |
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# e = d / ((area[None, :, None] + eps) * sigma) ** 2 / 2 # from formula |
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return (torch.exp(-e) * kpt_mask[:, None]).sum(-1) / (kpt_mask.sum(-1)[:, None] + eps) |
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def smooth_BCE(eps=0.1): |
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""" |
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Computes smoothed positive and negative Binary Cross-Entropy targets. |
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This function calculates positive and negative label smoothing BCE targets based on a given epsilon value. |
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For implementation details, refer to https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441. |
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Args: |
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eps (float, optional): The epsilon value for label smoothing. Defaults to 0.1. |
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Returns: |
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(tuple): A tuple containing the positive and negative label smoothing BCE targets. |
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""" |
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return 1.0 - 0.5 * eps, 0.5 * eps |
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class ConfusionMatrix: |
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""" |
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A class for calculating and updating a confusion matrix for object detection and classification tasks. |
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Attributes: |
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task (str): The type of task, either 'detect' or 'classify'. |
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matrix (np.array): The confusion matrix, with dimensions depending on the task. |
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nc (int): The number of classes. |
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conf (float): The confidence threshold for detections. |
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iou_thres (float): The Intersection over Union threshold. |
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""" |
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def __init__(self, nc, conf=0.25, iou_thres=0.45, task='detect'): |
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"""Initialize attributes for the YOLO model.""" |
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self.task = task |
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self.matrix = np.zeros((nc + 1, nc + 1)) if self.task == 'detect' else np.zeros((nc, nc)) |
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self.nc = nc # number of classes |
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self.conf = 0.25 if conf in (None, 0.001) else conf # apply 0.25 if default val conf is passed |
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self.iou_thres = iou_thres |
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def process_cls_preds(self, preds, targets): |
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""" |
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Update confusion matrix for classification task. |
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Args: |
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preds (Array[N, min(nc,5)]): Predicted class labels. |
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targets (Array[N, 1]): Ground truth class labels. |
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""" |
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preds, targets = torch.cat(preds)[:, 0], torch.cat(targets) |
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for p, t in zip(preds.cpu().numpy(), targets.cpu().numpy()): |
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self.matrix[p][t] += 1 |
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def process_batch(self, detections, labels): |
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""" |
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Update confusion matrix for object detection task. |
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Args: |
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detections (Array[N, 6]): Detected bounding boxes and their associated information. |
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Each row should contain (x1, y1, x2, y2, conf, class). |
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labels (Array[M, 5]): Ground truth bounding boxes and their associated class labels. |
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Each row should contain (class, x1, y1, x2, y2). |
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""" |
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if labels.size(0) == 0: # Check if labels is empty |
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if detections is not None: |
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detections = detections[detections[:, 4] > self.conf] |
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detection_classes = detections[:, 5].int() |
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for dc in detection_classes: |
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self.matrix[dc, self.nc] += 1 # false positives |
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return |
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if detections is None: |
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gt_classes = labels.int() |
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for gc in gt_classes: |
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self.matrix[self.nc, gc] += 1 # background FN |
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return |
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detections = detections[detections[:, 4] > self.conf] |
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gt_classes = labels[:, 0].int() |
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detection_classes = detections[:, 5].int() |
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iou = box_iou(labels[:, 1:], detections[:, :4]) |
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x = torch.where(iou > self.iou_thres) |
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if x[0].shape[0]: |
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matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() |
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if x[0].shape[0] > 1: |
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matches = matches[matches[:, 2].argsort()[::-1]] |
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matches = matches[np.unique(matches[:, 1], return_index=True)[1]] |
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matches = matches[matches[:, 2].argsort()[::-1]] |
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]] |
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else: |
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matches = np.zeros((0, 3)) |
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n = matches.shape[0] > 0 |
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m0, m1, _ = matches.transpose().astype(int) |
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for i, gc in enumerate(gt_classes): |
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j = m0 == i |
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if n and sum(j) == 1: |
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self.matrix[detection_classes[m1[j]], gc] += 1 # correct |
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else: |
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self.matrix[self.nc, gc] += 1 # true background |
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if n: |
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for i, dc in enumerate(detection_classes): |
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if not any(m1 == i): |
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self.matrix[dc, self.nc] += 1 # predicted background |
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def matrix(self): |
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"""Returns the confusion matrix.""" |
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return self.matrix |
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def tp_fp(self): |
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"""Returns true positives and false positives.""" |
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tp = self.matrix.diagonal() # true positives |
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fp = self.matrix.sum(1) - tp # false positives |
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# fn = self.matrix.sum(0) - tp # false negatives (missed detections) |
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return (tp[:-1], fp[:-1]) if self.task == 'detect' else (tp, fp) # remove background class if task=detect |
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@TryExcept('WARNING ⚠️ ConfusionMatrix plot failure') |
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@plt_settings() |
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def plot(self, normalize=True, save_dir='', names=(), on_plot=None): |
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""" |
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Plot the confusion matrix using seaborn and save it to a file. |
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Args: |
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normalize (bool): Whether to normalize the confusion matrix. |
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save_dir (str): Directory where the plot will be saved. |
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names (tuple): Names of classes, used as labels on the plot. |
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on_plot (func): An optional callback to pass plots path and data when they are rendered. |
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""" |
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import seaborn as sn |
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array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns |
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array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) |
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fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) |
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nc, nn = self.nc, len(names) # number of classes, names |
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sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size |
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labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels |
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ticklabels = (list(names) + ['background']) if labels else 'auto' |
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with warnings.catch_warnings(): |
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warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered |
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sn.heatmap(array, |
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ax=ax, |
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annot=nc < 30, |
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annot_kws={ |
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'size': 8}, |
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cmap='Blues', |
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fmt='.2f' if normalize else '.0f', |
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square=True, |
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vmin=0.0, |
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xticklabels=ticklabels, |
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yticklabels=ticklabels).set_facecolor((1, 1, 1)) |
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title = 'Confusion Matrix' + ' Normalized' * normalize |
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ax.set_xlabel('True') |
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ax.set_ylabel('Predicted') |
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ax.set_title(title) |
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plot_fname = Path(save_dir) / f'{title.lower().replace(" ", "_")}.png' |
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fig.savefig(plot_fname, dpi=250) |
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plt.close(fig) |
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if on_plot: |
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on_plot(plot_fname) |
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def print(self): |
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"""Print the confusion matrix to the console.""" |
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for i in range(self.nc + 1): |
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LOGGER.info(' '.join(map(str, self.matrix[i]))) |
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def smooth(y, f=0.05): |
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"""Box filter of fraction f.""" |
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nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) |
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p = np.ones(nf // 2) # ones padding |
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yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded |
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return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed |
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@plt_settings() |
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def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=(), on_plot=None): |
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"""Plots a precision-recall curve.""" |
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fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) |
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py = np.stack(py, axis=1) |
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if 0 < len(names) < 21: # display per-class legend if < 21 classes |
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for i, y in enumerate(py.T): |
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ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) |
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else: |
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ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) |
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ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) |
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ax.set_xlabel('Recall') |
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ax.set_ylabel('Precision') |
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ax.set_xlim(0, 1) |
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ax.set_ylim(0, 1) |
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ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') |
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ax.set_title('Precision-Recall Curve') |
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fig.savefig(save_dir, dpi=250) |
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plt.close(fig) |
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if on_plot: |
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on_plot(save_dir) |
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@plt_settings() |
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def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric', on_plot=None): |
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"""Plots a metric-confidence curve.""" |
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fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) |
|
|
|
|
|
if 0 < len(names) < 21: # display per-class legend if < 21 classes |
|
|
for i, y in enumerate(py): |
|
|
ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) |
|
|
else: |
|
|
ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) |
|
|
|
|
|
y = smooth(py.mean(0), 0.05) |
|
|
ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') |
|
|
ax.set_xlabel(xlabel) |
|
|
ax.set_ylabel(ylabel) |
|
|
ax.set_xlim(0, 1) |
|
|
ax.set_ylim(0, 1) |
|
|
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') |
|
|
ax.set_title(f'{ylabel}-Confidence Curve') |
|
|
fig.savefig(save_dir, dpi=250) |
|
|
plt.close(fig) |
|
|
if on_plot: |
|
|
on_plot(save_dir) |
|
|
|
|
|
|
|
|
def compute_ap(recall, precision): |
|
|
""" |
|
|
Compute the average precision (AP) given the recall and precision curves. |
|
|
|
|
|
Args: |
|
|
recall (list): The recall curve. |
|
|
precision (list): The precision curve. |
|
|
|
|
|
Returns: |
|
|
(float): Average precision. |
|
|
(np.ndarray): Precision envelope curve. |
|
|
(np.ndarray): Modified recall curve with sentinel values added at the beginning and end. |
|
|
""" |
|
|
|
|
|
# Append sentinel values to beginning and end |
|
|
mrec = np.concatenate(([0.0], recall, [1.0])) |
|
|
mpre = np.concatenate(([1.0], precision, [0.0])) |
|
|
|
|
|
# Compute the precision envelope |
|
|
mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) |
|
|
|
|
|
# Integrate area under curve |
|
|
method = 'interp' # methods: 'continuous', 'interp' |
|
|
if method == 'interp': |
|
|
x = np.linspace(0, 1, 101) # 101-point interp (COCO) |
|
|
ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate |
|
|
else: # 'continuous' |
|
|
i = np.where(mrec[1:] != mrec[:-1])[0] # points where x-axis (recall) changes |
|
|
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve |
|
|
|
|
|
return ap, mpre, mrec |
|
|
|
|
|
|
|
|
def ap_per_class(tp, |
|
|
conf, |
|
|
pred_cls, |
|
|
target_cls, |
|
|
plot=False, |
|
|
on_plot=None, |
|
|
save_dir=Path(), |
|
|
names=(), |
|
|
eps=1e-16, |
|
|
prefix=''): |
|
|
""" |
|
|
Computes the average precision per class for object detection evaluation. |
|
|
|
|
|
Args: |
|
|
tp (np.ndarray): Binary array indicating whether the detection is correct (True) or not (False). |
|
|
conf (np.ndarray): Array of confidence scores of the detections. |
|
|
pred_cls (np.ndarray): Array of predicted classes of the detections. |
|
|
target_cls (np.ndarray): Array of true classes of the detections. |
|
|
plot (bool, optional): Whether to plot PR curves or not. Defaults to False. |
|
|
on_plot (func, optional): A callback to pass plots path and data when they are rendered. Defaults to None. |
|
|
save_dir (Path, optional): Directory to save the PR curves. Defaults to an empty path. |
|
|
names (tuple, optional): Tuple of class names to plot PR curves. Defaults to an empty tuple. |
|
|
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-16. |
|
|
prefix (str, optional): A prefix string for saving the plot files. Defaults to an empty string. |
|
|
|
|
|
Returns: |
|
|
(tuple): A tuple of six arrays and one array of unique classes, where: |
|
|
tp (np.ndarray): True positive counts at threshold given by max F1 metric for each class.Shape: (nc,). |
|
|
fp (np.ndarray): False positive counts at threshold given by max F1 metric for each class. Shape: (nc,). |
|
|
p (np.ndarray): Precision values at threshold given by max F1 metric for each class. Shape: (nc,). |
|
|
r (np.ndarray): Recall values at threshold given by max F1 metric for each class. Shape: (nc,). |
|
|
f1 (np.ndarray): F1-score values at threshold given by max F1 metric for each class. Shape: (nc,). |
|
|
ap (np.ndarray): Average precision for each class at different IoU thresholds. Shape: (nc, 10). |
|
|
unique_classes (np.ndarray): An array of unique classes that have data. Shape: (nc,). |
|
|
p_curve (np.ndarray): Precision curves for each class. Shape: (nc, 1000). |
|
|
r_curve (np.ndarray): Recall curves for each class. Shape: (nc, 1000). |
|
|
f1_curve (np.ndarray): F1-score curves for each class. Shape: (nc, 1000). |
|
|
x (np.ndarray): X-axis values for the curves. Shape: (1000,). |
|
|
prec_values: Precision values at mAP@0.5 for each class. Shape: (nc, 1000). |
|
|
""" |
|
|
|
|
|
# Sort by objectness |
|
|
i = np.argsort(-conf) |
|
|
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] |
|
|
|
|
|
# Find unique classes |
|
|
unique_classes, nt = np.unique(target_cls, return_counts=True) |
|
|
nc = unique_classes.shape[0] # number of classes, number of detections |
|
|
|
|
|
# Create Precision-Recall curve and compute AP for each class |
|
|
x, prec_values = np.linspace(0, 1, 1000), [] |
|
|
|
|
|
# Average precision, precision and recall curves |
|
|
ap, p_curve, r_curve = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) |
|
|
for ci, c in enumerate(unique_classes): |
|
|
i = pred_cls == c |
|
|
n_l = nt[ci] # number of labels |
|
|
n_p = i.sum() # number of predictions |
|
|
if n_p == 0 or n_l == 0: |
|
|
continue |
|
|
|
|
|
# Accumulate FPs and TPs |
|
|
fpc = (1 - tp[i]).cumsum(0) |
|
|
tpc = tp[i].cumsum(0) |
|
|
|
|
|
# Recall |
|
|
recall = tpc / (n_l + eps) # recall curve |
|
|
r_curve[ci] = np.interp(-x, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases |
|
|
|
|
|
# Precision |
|
|
precision = tpc / (tpc + fpc) # precision curve |
|
|
p_curve[ci] = np.interp(-x, -conf[i], precision[:, 0], left=1) # p at pr_score |
|
|
|
|
|
# AP from recall-precision curve |
|
|
for j in range(tp.shape[1]): |
|
|
ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) |
|
|
if plot and j == 0: |
|
|
prec_values.append(np.interp(x, mrec, mpre)) # precision at mAP@0.5 |
|
|
|
|
|
prec_values = np.array(prec_values) # (nc, 1000) |
|
|
|
|
|
# Compute F1 (harmonic mean of precision and recall) |
|
|
f1_curve = 2 * p_curve * r_curve / (p_curve + r_curve + eps) |
|
|
names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data |
|
|
names = dict(enumerate(names)) # to dict |
|
|
if plot: |
|
|
plot_pr_curve(x, prec_values, ap, save_dir / f'{prefix}PR_curve.png', names, on_plot=on_plot) |
|
|
plot_mc_curve(x, f1_curve, save_dir / f'{prefix}F1_curve.png', names, ylabel='F1', on_plot=on_plot) |
|
|
plot_mc_curve(x, p_curve, save_dir / f'{prefix}P_curve.png', names, ylabel='Precision', on_plot=on_plot) |
|
|
plot_mc_curve(x, r_curve, save_dir / f'{prefix}R_curve.png', names, ylabel='Recall', on_plot=on_plot) |
|
|
|
|
|
i = smooth(f1_curve.mean(0), 0.1).argmax() # max F1 index |
|
|
p, r, f1 = p_curve[:, i], r_curve[:, i], f1_curve[:, i] # max-F1 precision, recall, F1 values |
|
|
tp = (r * nt).round() # true positives |
|
|
fp = (tp / (p + eps) - tp).round() # false positives |
|
|
return tp, fp, p, r, f1, ap, unique_classes.astype(int), p_curve, r_curve, f1_curve, x, prec_values |
|
|
|
|
|
|
|
|
class Metric(SimpleClass): |
|
|
""" |
|
|
Class for computing evaluation metrics for YOLOv8 model. |
|
|
|
|
|
Attributes: |
|
|
p (list): Precision for each class. Shape: (nc,). |
|
|
r (list): Recall for each class. Shape: (nc,). |
|
|
f1 (list): F1 score for each class. Shape: (nc,). |
|
|
all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10). |
|
|
ap_class_index (list): Index of class for each AP score. Shape: (nc,). |
|
|
nc (int): Number of classes. |
|
|
|
|
|
Methods: |
|
|
ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or []. |
|
|
ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or []. |
|
|
mp(): Mean precision of all classes. Returns: Float. |
|
|
mr(): Mean recall of all classes. Returns: Float. |
|
|
map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float. |
|
|
map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float. |
|
|
map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float. |
|
|
mean_results(): Mean of results, returns mp, mr, map50, map. |
|
|
class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i]. |
|
|
maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,). |
|
|
fitness(): Model fitness as a weighted combination of metrics. Returns: Float. |
|
|
update(results): Update metric attributes with new evaluation results. |
|
|
""" |
|
|
|
|
|
def __init__(self) -> None: |
|
|
"""Initializes a Metric instance for computing evaluation metrics for the YOLOv8 model.""" |
|
|
self.p = [] # (nc, ) |
|
|
self.r = [] # (nc, ) |
|
|
self.f1 = [] # (nc, ) |
|
|
self.all_ap = [] # (nc, 10) |
|
|
self.ap_class_index = [] # (nc, ) |
|
|
self.nc = 0 |
|
|
|
|
|
@property |
|
|
def ap50(self): |
|
|
""" |
|
|
Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes. |
|
|
|
|
|
Returns: |
|
|
(np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. |
|
|
""" |
|
|
return self.all_ap[:, 0] if len(self.all_ap) else [] |
|
|
|
|
|
@property |
|
|
def ap(self): |
|
|
""" |
|
|
Returns the Average Precision (AP) at an IoU threshold of 0.5-0.95 for all classes. |
|
|
|
|
|
Returns: |
|
|
(np.ndarray, list): Array of shape (nc,) with AP50-95 values per class, or an empty list if not available. |
|
|
""" |
|
|
return self.all_ap.mean(1) if len(self.all_ap) else [] |
|
|
|
|
|
@property |
|
|
def mp(self): |
|
|
""" |
|
|
Returns the Mean Precision of all classes. |
|
|
|
|
|
Returns: |
|
|
(float): The mean precision of all classes. |
|
|
""" |
|
|
return self.p.mean() if len(self.p) else 0.0 |
|
|
|
|
|
@property |
|
|
def mr(self): |
|
|
""" |
|
|
Returns the Mean Recall of all classes. |
|
|
|
|
|
Returns: |
|
|
(float): The mean recall of all classes. |
|
|
""" |
|
|
return self.r.mean() if len(self.r) else 0.0 |
|
|
|
|
|
@property |
|
|
def map50(self): |
|
|
""" |
|
|
Returns the mean Average Precision (mAP) at an IoU threshold of 0.5. |
|
|
|
|
|
Returns: |
|
|
(float): The mAP50 at an IoU threshold of 0.5. |
|
|
""" |
|
|
return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 |
|
|
|
|
|
@property |
|
|
def map75(self): |
|
|
""" |
|
|
Returns the mean Average Precision (mAP) at an IoU threshold of 0.75. |
|
|
|
|
|
Returns: |
|
|
(float): The mAP50 at an IoU threshold of 0.75. |
|
|
""" |
|
|
return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0 |
|
|
|
|
|
@property |
|
|
def map(self): |
|
|
""" |
|
|
Returns the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05. |
|
|
|
|
|
Returns: |
|
|
(float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05. |
|
|
""" |
|
|
return self.all_ap.mean() if len(self.all_ap) else 0.0 |
|
|
|
|
|
def mean_results(self): |
|
|
"""Mean of results, return mp, mr, map50, map.""" |
|
|
return [self.mp, self.mr, self.map50, self.map] |
|
|
|
|
|
def class_result(self, i): |
|
|
"""Class-aware result, return p[i], r[i], ap50[i], ap[i].""" |
|
|
return self.p[i], self.r[i], self.ap50[i], self.ap[i] |
|
|
|
|
|
@property |
|
|
def maps(self): |
|
|
"""MAP of each class.""" |
|
|
maps = np.zeros(self.nc) + self.map |
|
|
for i, c in enumerate(self.ap_class_index): |
|
|
maps[c] = self.ap[i] |
|
|
return maps |
|
|
|
|
|
def fitness(self): |
|
|
"""Model fitness as a weighted combination of metrics.""" |
|
|
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] |
|
|
return (np.array(self.mean_results()) * w).sum() |
|
|
|
|
|
def update(self, results): |
|
|
""" |
|
|
Updates the evaluation metrics of the model with a new set of results. |
|
|
|
|
|
Args: |
|
|
results (tuple): A tuple containing the following evaluation metrics: |
|
|
- p (list): Precision for each class. Shape: (nc,). |
|
|
- r (list): Recall for each class. Shape: (nc,). |
|
|
- f1 (list): F1 score for each class. Shape: (nc,). |
|
|
- all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10). |
|
|
- ap_class_index (list): Index of class for each AP score. Shape: (nc,). |
|
|
|
|
|
Side Effects: |
|
|
Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based |
|
|
on the values provided in the `results` tuple. |
|
|
""" |
|
|
(self.p, self.r, self.f1, self.all_ap, self.ap_class_index, self.p_curve, self.r_curve, self.f1_curve, self.px, |
|
|
self.prec_values) = results |
|
|
|
|
|
@property |
|
|
def curves(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return [] |
|
|
|
|
|
@property |
|
|
def curves_results(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return [[self.px, self.prec_values, 'Recall', 'Precision'], [self.px, self.f1_curve, 'Confidence', 'F1'], |
|
|
[self.px, self.p_curve, 'Confidence', 'Precision'], [self.px, self.r_curve, 'Confidence', 'Recall']] |
|
|
|
|
|
|
|
|
class DetMetrics(SimpleClass): |
|
|
""" |
|
|
This class is a utility class for computing detection metrics such as precision, recall, and mean average precision |
|
|
(mAP) of an object detection model. |
|
|
|
|
|
Args: |
|
|
save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory. |
|
|
plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False. |
|
|
on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. |
|
|
names (tuple of str): A tuple of strings that represents the names of the classes. Defaults to an empty tuple. |
|
|
|
|
|
Attributes: |
|
|
save_dir (Path): A path to the directory where the output plots will be saved. |
|
|
plot (bool): A flag that indicates whether to plot the precision-recall curves for each class. |
|
|
on_plot (func): An optional callback to pass plots path and data when they are rendered. |
|
|
names (tuple of str): A tuple of strings that represents the names of the classes. |
|
|
box (Metric): An instance of the Metric class for storing the results of the detection metrics. |
|
|
speed (dict): A dictionary for storing the execution time of different parts of the detection process. |
|
|
|
|
|
Methods: |
|
|
process(tp, conf, pred_cls, target_cls): Updates the metric results with the latest batch of predictions. |
|
|
keys: Returns a list of keys for accessing the computed detection metrics. |
|
|
mean_results: Returns a list of mean values for the computed detection metrics. |
|
|
class_result(i): Returns a list of values for the computed detection metrics for a specific class. |
|
|
maps: Returns a dictionary of mean average precision (mAP) values for different IoU thresholds. |
|
|
fitness: Computes the fitness score based on the computed detection metrics. |
|
|
ap_class_index: Returns a list of class indices sorted by their average precision (AP) values. |
|
|
results_dict: Returns a dictionary that maps detection metric keys to their computed values. |
|
|
curves: TODO |
|
|
curves_results: TODO |
|
|
""" |
|
|
|
|
|
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None: |
|
|
"""Initialize a DetMetrics instance with a save directory, plot flag, callback function, and class names.""" |
|
|
self.save_dir = save_dir |
|
|
self.plot = plot |
|
|
self.on_plot = on_plot |
|
|
self.names = names |
|
|
self.box = Metric() |
|
|
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0} |
|
|
self.task = 'detect' |
|
|
|
|
|
def process(self, tp, conf, pred_cls, target_cls): |
|
|
"""Process predicted results for object detection and update metrics.""" |
|
|
results = ap_per_class(tp, |
|
|
conf, |
|
|
pred_cls, |
|
|
target_cls, |
|
|
plot=self.plot, |
|
|
save_dir=self.save_dir, |
|
|
names=self.names, |
|
|
on_plot=self.on_plot)[2:] |
|
|
self.box.nc = len(self.names) |
|
|
self.box.update(results) |
|
|
|
|
|
@property |
|
|
def keys(self): |
|
|
"""Returns a list of keys for accessing specific metrics.""" |
|
|
return ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)'] |
|
|
|
|
|
def mean_results(self): |
|
|
"""Calculate mean of detected objects & return precision, recall, mAP50, and mAP50-95.""" |
|
|
return self.box.mean_results() |
|
|
|
|
|
def class_result(self, i): |
|
|
"""Return the result of evaluating the performance of an object detection model on a specific class.""" |
|
|
return self.box.class_result(i) |
|
|
|
|
|
@property |
|
|
def maps(self): |
|
|
"""Returns mean Average Precision (mAP) scores per class.""" |
|
|
return self.box.maps |
|
|
|
|
|
@property |
|
|
def fitness(self): |
|
|
"""Returns the fitness of box object.""" |
|
|
return self.box.fitness() |
|
|
|
|
|
@property |
|
|
def ap_class_index(self): |
|
|
"""Returns the average precision index per class.""" |
|
|
return self.box.ap_class_index |
|
|
|
|
|
@property |
|
|
def results_dict(self): |
|
|
"""Returns dictionary of computed performance metrics and statistics.""" |
|
|
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness])) |
|
|
|
|
|
@property |
|
|
def curves(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)'] |
|
|
|
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|
@property |
|
|
def curves_results(self): |
|
|
"""Returns dictionary of computed performance metrics and statistics.""" |
|
|
return self.box.curves_results |
|
|
|
|
|
|
|
|
class SegmentMetrics(SimpleClass): |
|
|
""" |
|
|
Calculates and aggregates detection and segmentation metrics over a given set of classes. |
|
|
|
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|
Args: |
|
|
save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory. |
|
|
plot (bool): Whether to save the detection and segmentation plots. Default is False. |
|
|
on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. |
|
|
names (list): List of class names. Default is an empty list. |
|
|
|
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|
Attributes: |
|
|
save_dir (Path): Path to the directory where the output plots should be saved. |
|
|
plot (bool): Whether to save the detection and segmentation plots. |
|
|
on_plot (func): An optional callback to pass plots path and data when they are rendered. |
|
|
names (list): List of class names. |
|
|
box (Metric): An instance of the Metric class to calculate box detection metrics. |
|
|
seg (Metric): An instance of the Metric class to calculate mask segmentation metrics. |
|
|
speed (dict): Dictionary to store the time taken in different phases of inference. |
|
|
|
|
|
Methods: |
|
|
process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions. |
|
|
mean_results(): Returns the mean of the detection and segmentation metrics over all the classes. |
|
|
class_result(i): Returns the detection and segmentation metrics of class `i`. |
|
|
maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. |
|
|
fitness: Returns the fitness scores, which are a single weighted combination of metrics. |
|
|
ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP). |
|
|
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score. |
|
|
""" |
|
|
|
|
|
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None: |
|
|
"""Initialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names.""" |
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|
self.save_dir = save_dir |
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|
self.plot = plot |
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|
self.on_plot = on_plot |
|
|
self.names = names |
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|
self.box = Metric() |
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|
self.seg = Metric() |
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|
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0} |
|
|
self.task = 'segment' |
|
|
|
|
|
def process(self, tp_b, tp_m, conf, pred_cls, target_cls): |
|
|
""" |
|
|
Processes the detection and segmentation metrics over the given set of predictions. |
|
|
|
|
|
Args: |
|
|
tp_b (list): List of True Positive boxes. |
|
|
tp_m (list): List of True Positive masks. |
|
|
conf (list): List of confidence scores. |
|
|
pred_cls (list): List of predicted classes. |
|
|
target_cls (list): List of target classes. |
|
|
""" |
|
|
|
|
|
results_mask = ap_per_class(tp_m, |
|
|
conf, |
|
|
pred_cls, |
|
|
target_cls, |
|
|
plot=self.plot, |
|
|
on_plot=self.on_plot, |
|
|
save_dir=self.save_dir, |
|
|
names=self.names, |
|
|
prefix='Mask')[2:] |
|
|
self.seg.nc = len(self.names) |
|
|
self.seg.update(results_mask) |
|
|
results_box = ap_per_class(tp_b, |
|
|
conf, |
|
|
pred_cls, |
|
|
target_cls, |
|
|
plot=self.plot, |
|
|
on_plot=self.on_plot, |
|
|
save_dir=self.save_dir, |
|
|
names=self.names, |
|
|
prefix='Box')[2:] |
|
|
self.box.nc = len(self.names) |
|
|
self.box.update(results_box) |
|
|
|
|
|
@property |
|
|
def keys(self): |
|
|
"""Returns a list of keys for accessing metrics.""" |
|
|
return [ |
|
|
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', |
|
|
'metrics/precision(M)', 'metrics/recall(M)', 'metrics/mAP50(M)', 'metrics/mAP50-95(M)'] |
|
|
|
|
|
def mean_results(self): |
|
|
"""Return the mean metrics for bounding box and segmentation results.""" |
|
|
return self.box.mean_results() + self.seg.mean_results() |
|
|
|
|
|
def class_result(self, i): |
|
|
"""Returns classification results for a specified class index.""" |
|
|
return self.box.class_result(i) + self.seg.class_result(i) |
|
|
|
|
|
@property |
|
|
def maps(self): |
|
|
"""Returns mAP scores for object detection and semantic segmentation models.""" |
|
|
return self.box.maps + self.seg.maps |
|
|
|
|
|
@property |
|
|
def fitness(self): |
|
|
"""Get the fitness score for both segmentation and bounding box models.""" |
|
|
return self.seg.fitness() + self.box.fitness() |
|
|
|
|
|
@property |
|
|
def ap_class_index(self): |
|
|
"""Boxes and masks have the same ap_class_index.""" |
|
|
return self.box.ap_class_index |
|
|
|
|
|
@property |
|
|
def results_dict(self): |
|
|
"""Returns results of object detection model for evaluation.""" |
|
|
return dict(zip(self.keys + ['fitness'], self.mean_results() + [self.fitness])) |
|
|
|
|
|
@property |
|
|
def curves(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return [ |
|
|
'Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)', |
|
|
'Precision-Recall(M)', 'F1-Confidence(M)', 'Precision-Confidence(M)', 'Recall-Confidence(M)'] |
|
|
|
|
|
@property |
|
|
def curves_results(self): |
|
|
"""Returns dictionary of computed performance metrics and statistics.""" |
|
|
return self.box.curves_results + self.seg.curves_results |
|
|
|
|
|
|
|
|
class PoseMetrics(SegmentMetrics): |
|
|
""" |
|
|
Calculates and aggregates detection and pose metrics over a given set of classes. |
|
|
|
|
|
Args: |
|
|
save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory. |
|
|
plot (bool): Whether to save the detection and segmentation plots. Default is False. |
|
|
on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. |
|
|
names (list): List of class names. Default is an empty list. |
|
|
|
|
|
Attributes: |
|
|
save_dir (Path): Path to the directory where the output plots should be saved. |
|
|
plot (bool): Whether to save the detection and segmentation plots. |
|
|
on_plot (func): An optional callback to pass plots path and data when they are rendered. |
|
|
names (list): List of class names. |
|
|
box (Metric): An instance of the Metric class to calculate box detection metrics. |
|
|
pose (Metric): An instance of the Metric class to calculate mask segmentation metrics. |
|
|
speed (dict): Dictionary to store the time taken in different phases of inference. |
|
|
|
|
|
Methods: |
|
|
process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions. |
|
|
mean_results(): Returns the mean of the detection and segmentation metrics over all the classes. |
|
|
class_result(i): Returns the detection and segmentation metrics of class `i`. |
|
|
maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. |
|
|
fitness: Returns the fitness scores, which are a single weighted combination of metrics. |
|
|
ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP). |
|
|
results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score. |
|
|
""" |
|
|
|
|
|
def __init__(self, save_dir=Path('.'), plot=False, on_plot=None, names=()) -> None: |
|
|
"""Initialize the PoseMetrics class with directory path, class names, and plotting options.""" |
|
|
super().__init__(save_dir, plot, names) |
|
|
self.save_dir = save_dir |
|
|
self.plot = plot |
|
|
self.on_plot = on_plot |
|
|
self.names = names |
|
|
self.box = Metric() |
|
|
self.pose = Metric() |
|
|
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0} |
|
|
self.task = 'pose' |
|
|
|
|
|
def process(self, tp_b, tp_p, conf, pred_cls, target_cls): |
|
|
""" |
|
|
Processes the detection and pose metrics over the given set of predictions. |
|
|
|
|
|
Args: |
|
|
tp_b (list): List of True Positive boxes. |
|
|
tp_p (list): List of True Positive keypoints. |
|
|
conf (list): List of confidence scores. |
|
|
pred_cls (list): List of predicted classes. |
|
|
target_cls (list): List of target classes. |
|
|
""" |
|
|
|
|
|
results_pose = ap_per_class(tp_p, |
|
|
conf, |
|
|
pred_cls, |
|
|
target_cls, |
|
|
plot=self.plot, |
|
|
on_plot=self.on_plot, |
|
|
save_dir=self.save_dir, |
|
|
names=self.names, |
|
|
prefix='Pose')[2:] |
|
|
self.pose.nc = len(self.names) |
|
|
self.pose.update(results_pose) |
|
|
results_box = ap_per_class(tp_b, |
|
|
conf, |
|
|
pred_cls, |
|
|
target_cls, |
|
|
plot=self.plot, |
|
|
on_plot=self.on_plot, |
|
|
save_dir=self.save_dir, |
|
|
names=self.names, |
|
|
prefix='Box')[2:] |
|
|
self.box.nc = len(self.names) |
|
|
self.box.update(results_box) |
|
|
|
|
|
@property |
|
|
def keys(self): |
|
|
"""Returns list of evaluation metric keys.""" |
|
|
return [ |
|
|
'metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)', |
|
|
'metrics/precision(P)', 'metrics/recall(P)', 'metrics/mAP50(P)', 'metrics/mAP50-95(P)'] |
|
|
|
|
|
def mean_results(self): |
|
|
"""Return the mean results of box and pose.""" |
|
|
return self.box.mean_results() + self.pose.mean_results() |
|
|
|
|
|
def class_result(self, i): |
|
|
"""Return the class-wise detection results for a specific class i.""" |
|
|
return self.box.class_result(i) + self.pose.class_result(i) |
|
|
|
|
|
@property |
|
|
def maps(self): |
|
|
"""Returns the mean average precision (mAP) per class for both box and pose detections.""" |
|
|
return self.box.maps + self.pose.maps |
|
|
|
|
|
@property |
|
|
def fitness(self): |
|
|
"""Computes classification metrics and speed using the `targets` and `pred` inputs.""" |
|
|
return self.pose.fitness() + self.box.fitness() |
|
|
|
|
|
@property |
|
|
def curves(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return [ |
|
|
'Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)', |
|
|
'Precision-Recall(P)', 'F1-Confidence(P)', 'Precision-Confidence(P)', 'Recall-Confidence(P)'] |
|
|
|
|
|
@property |
|
|
def curves_results(self): |
|
|
"""Returns dictionary of computed performance metrics and statistics.""" |
|
|
return self.box.curves_results + self.pose.curves_results |
|
|
|
|
|
|
|
|
class ClassifyMetrics(SimpleClass): |
|
|
""" |
|
|
Class for computing classification metrics including top-1 and top-5 accuracy. |
|
|
|
|
|
Attributes: |
|
|
top1 (float): The top-1 accuracy. |
|
|
top5 (float): The top-5 accuracy. |
|
|
speed (Dict[str, float]): A dictionary containing the time taken for each step in the pipeline. |
|
|
|
|
|
Properties: |
|
|
fitness (float): The fitness of the model, which is equal to top-5 accuracy. |
|
|
results_dict (Dict[str, Union[float, str]]): A dictionary containing the classification metrics and fitness. |
|
|
keys (List[str]): A list of keys for the results_dict. |
|
|
|
|
|
Methods: |
|
|
process(targets, pred): Processes the targets and predictions to compute classification metrics. |
|
|
""" |
|
|
|
|
|
def __init__(self) -> None: |
|
|
"""Initialize a ClassifyMetrics instance.""" |
|
|
self.top1 = 0 |
|
|
self.top5 = 0 |
|
|
self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0} |
|
|
self.task = 'classify' |
|
|
|
|
|
def process(self, targets, pred): |
|
|
"""Target classes and predicted classes.""" |
|
|
pred, targets = torch.cat(pred), torch.cat(targets) |
|
|
correct = (targets[:, None] == pred).float() |
|
|
acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy |
|
|
self.top1, self.top5 = acc.mean(0).tolist() |
|
|
|
|
|
@property |
|
|
def fitness(self): |
|
|
"""Returns mean of top-1 and top-5 accuracies as fitness score.""" |
|
|
return (self.top1 + self.top5) / 2 |
|
|
|
|
|
@property |
|
|
def results_dict(self): |
|
|
"""Returns a dictionary with model's performance metrics and fitness score.""" |
|
|
return dict(zip(self.keys + ['fitness'], [self.top1, self.top5, self.fitness])) |
|
|
|
|
|
@property |
|
|
def keys(self): |
|
|
"""Returns a list of keys for the results_dict property.""" |
|
|
return ['metrics/accuracy_top1', 'metrics/accuracy_top5'] |
|
|
|
|
|
@property |
|
|
def curves(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return [] |
|
|
|
|
|
@property |
|
|
def curves_results(self): |
|
|
"""Returns a list of curves for accessing specific metrics curves.""" |
|
|
return []
|
|
|
|
