importtorch# Modelmodel=torch.hub.load('ultralytics/yolov5','yolov5s')# Imageimg='https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'# Inferenceresults=model(img)results.print()# or .show(), .save() Training ...
sum() / target_scores_sum else: loss_dfl = torch.tensor(0.0).to(pred_dist.device) return loss_iou, loss_dfl @staticmethod def _df_loss(pred_dist, target): """ 计算分布焦点损失 (DFL)。 参数: - pred_dist: 预测的分布 - target: 目标分布 返回: - DFL 损失 """ tl = target.long(...
importtorch.nn.functionalasF defdouble_convolution(in_channels, out_channels): """ In the original paper implementation, the convolution operations were not padded but we are padding them here. This is because, we need the output result size to be same as input size. ...
b,c] or a single model weights=[a] or weights=a from models.yolo import Detect, Model model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: ckpt = torch.load(w, map_location='cpu') # load ckpt = (ckpt.get('ema') or ckpt[...
import torch from torchmetrics import Metric class MyAccuracy(Metric): def __init__(self): # remember to call super super().__init__() # call `self.add_state`for every internal state that is needed for the metrics computations # dist_reduce_fx indicates the function that should be used...
💡 Your Question No matter what I do the output is always of type: numpy.ndarray import torch import numpy as np from super_gradients.common.object_names import Models from super_gradients.training import models rgb = np.random.rand(640, ...
self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot) self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95 self.niou = self.iouv.numel() self.lb = [] # for autolabellingdef preprocess(self, batch): ...
🐛 Bug description Unable to use Metric for calculating roc_auc_compute Code that caused error: from typing import Any, Callable, Tuple import torch from ignite.metrics import EpochMetric def roc_auc_compute_fn(y_preds: torch.Tensor, y_ta...
import os from collections import OrderedDict import PIL.Image as Image import torch from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.data import MetadataCatalog from detectron2.utils.comm import all_gather, is_main_process, synchronize from detectron2.utils.file_io import Pa...
data.utils import check_det_dataset from ultralytics.utils.checks import check_requirements, check_imgsz, check_file from ultralytics.utils.metrics import ConfusionMatrix, DetMetrics, box_iou from ultralytics.utils.plotting import output_to_target, plot_images from ultralytics.utils.torch_utils ...