![]() ![]() please use regional emissions factors available in AVERT or eGRID. Note that the calculator uses national average emissions factors for electricity, which may not be accurate for your region. For electricity consumption, the calculator uses an average emissions factor that includes both baseload and non-baseload generation. * The Equivalencies Calculator uses different emissions factors for electricity depending on whether it is avoided or consumed at typical scales, energy efficiency and renewable energy programs and projects do not affect baseload power generation, so the calculator uses a non-baseload emissions factor. For more accurate estimates, please use regional emissions factors available in AVERT or eGRID. Kilowatt-hours used Choose kilowatt-hours used when entering data on electricity use, such as your household’s or company’s annual electricity consumption. Kilowatt-hours avoided Choose kilowatt-hours avoided when entering data on electricity use avoided through energy efficiency or fossil fuel electricity generation avoided through renewable energy. To see the methodology used to determine annual greenhouse gas emissions per passenger vehicle, visit the Calculations & References page for equations and sources used. For the calculator’s purposes, passenger vehicles are defined as 2-axle 4-tire vehicles, including passenger cars, vans, pickup trucks, and sport/utility vehicles. Preds = pred.Gasoline-powered passenger vehicles While passenger vehicles are not a unit of energy consumption, they do consume energy. Outputs = outputs.permute(0, 2, 3, 4, 1).contiguous() Images, labels, filename = dataiter.next() Test_loader = (dataset=dset_test,ĭata_info = pd.read_csv(filelist_name_test, header=None) Ious.append(float(intersection) / float(max(union, 1)))įilelist_name_test = '/path/to/my/test/filelist.txt'ĭset_test = myPytorchDatasetClass.CustomDataset(filelist_name_test, data_root_test) Cable Calculator Voltage Load Allowable Voltage Drop () Cable Length (m) Required Cable Size (mm2) Voltage Drop (volts) Percentage Drop () Load (Amps). Ious.append(float('nan')) # If there is no ground truth, do not include in evaluation ![]() Union = pred_inds.long().sum().data.cpu() + target_inds.long().sum().data.cpu() - intersection Intersection = (pred_inds).long().sum().data.cpu() # Cast to long to prevent overflows Import myPytorchDatasetClass # Custom dataset class, inherited from įor cls in xrange(1, n_classes): # This goes from 1:n_classes-1 -> class "0" is ignored Import pandas as pd # For filelist reading The wrapping function evaluate_performance is not universal, but it shows that one needs to iterate over all results before computing IoU. The key function here is the function called iou. I'll post the link if I can find it again. I found this somewhere and adapted it for me. Official example: > from torchmetrics import JaccardIndex LIGHTNING CALCULATOR: 'Those who are considered genius' with mathematics are otherwise known as lightning calculators.' DECENTRALIZED ORGANIZATION PLEASURE PRINCIPLE a person of typically high IQ who is capable of processing numerical data at a lightning fast speed to complete calculations. If preds has an extra dimension as in the case of multi-class scores we perform an argmax on dim=1. This is the case for binary and multi-label probabilities. Self.threshold argument to convert into integer labels. ) If preds and targetĪre the same shape and preds is a float tensor, we use the Works with multi-dimensional preds and target. We’ll start by adding a few useful classification metrics to the MNIST example we started with earlier. Accepts probabilities from a model output or integer class values in prediction. Works with binary, multiclass and multi-label data. They may be subject to conversion from input data (see description below). Where: A and B are both tensors of the same size, containing integer class values. Torchmetrics.JaccardIndex(num_classes, ignore_index=None, absent_score=0.0, threshold=0.5, multilabel=False, reduction='elementwise_mean', compute_on_step=None, **kwargs)Ĭomputes Intersection over union, or Jaccard index calculation: It works with PyTorch and PyTorch Lightning, also with distributed training. ![]() It is named torchmetrics.JaccardIndex (previously torchmetrics.IoU) and calculates what you want. As of 2021, there's no need to implement your own IoU, as torchmetrics comes equipped with it - here's the link. ![]()
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