109 references to RangeUtil
Microsoft.ML.TorchSharp (109)
AutoFormerV2\Anchors.cs (11)
106anchors[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(2..)] = baseSize * torch.tile(scales, new long[] { 2, ratios.Length }).transpose(1, 0); 109var areas = torch.mul(anchors[RangeUtil.ToTensorIndex(..), 2], anchors[RangeUtil.ToTensorIndex(..), 3]); 112anchors[RangeUtil.ToTensorIndex(..), 2] = torch.sqrt(areas / torch.repeat_interleave(ratios, new long[] { scales.Length })); 113anchors[RangeUtil.ToTensorIndex(..), 3] = torch.mul(anchors[RangeUtil.ToTensorIndex(..), 2], torch.repeat_interleave(ratios, new long[] { scales.Length })); 116anchors[RangeUtil.ToTensorIndex(..), torch.TensorIndex.Tensor(torch.tensor(new long[] { 0, 2 }, dtype: torch.int64))] -= torch.tile(anchors[RangeUtil.ToTensorIndex(..), 2] * 0.5, new long[] { 2, 1 }).T; 117anchors[RangeUtil.ToTensorIndex(..), torch.TensorIndex.Tensor(torch.tensor(new long[] { 1, 3 }, dtype: torch.int64))] -= torch.tile(anchors[RangeUtil.ToTensorIndex(..), 3] * 0.5, new long[] { 2, 1 }).T;
AutoFormerV2\Attention.cs (1)
116var attn = (torch.matmul(q, k.transpose(-2, -1)) * this.scale) + this.attention_biases[RangeUtil.ToTensorIndex(..), this.attention_bias_idxs];
AutoFormerV2\AutoFormerV2Block.cs (3)
130x = x[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..h), RangeUtil.ToTensorIndex(..w)].contiguous();
AutoFormerV2\ObjectDetectionTrainer.cs (13)
387transMidTensor[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..image.Height), RangeUtil.ToTensorIndex(..image.Width)] = reMidTensor / 255.0; 410labelTensor[RangeUtil.ToTensorIndex(..), i, 0] = x0; 411labelTensor[RangeUtil.ToTensorIndex(..), i, 1] = y0; 412labelTensor[RangeUtil.ToTensorIndex(..), i, 2] = x1; 413labelTensor[RangeUtil.ToTensorIndex(..), i, 3] = y1; 414labelTensor[RangeUtil.ToTensorIndex(..), i, 4] = cl; 922transMidTensor[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..image.Height), RangeUtil.ToTensorIndex(..image.Width)] = reMidTensor / 255.0;
Loss\FocalLoss.cs (41)
43var anchor = anchors[0, RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..)]; 45var anchorWidths = anchor[RangeUtil.ToTensorIndex(..), 2] - anchor[RangeUtil.ToTensorIndex(..), 0]; 46var anchorHeights = anchor[RangeUtil.ToTensorIndex(..), 3] - anchor[RangeUtil.ToTensorIndex(..), 1]; 47var anchorCtrX = anchor[RangeUtil.ToTensorIndex(..), 0] + (0.5 * anchorWidths); 48var anchorCtrY = anchor[RangeUtil.ToTensorIndex(..), 1] + (0.5 * anchorHeights); 52var classification = classifications[j, RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..)]; 53var regression = regressions[j, RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..)]; 55var bboxAnnotation = annotations[j, RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..)]; 56bboxAnnotation = bboxAnnotation[bboxAnnotation[RangeUtil.ToTensorIndex(..), 4] != -1]; 76var iou = CalcIou(anchors[0, RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..)], bboxAnnotation[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..4)]); // num_anchors x num_annotations 128var gtWidths = assignedAnnotations[RangeUtil.ToTensorIndex(..), 2] - assignedAnnotations[RangeUtil.ToTensorIndex(..), 0]; 129var gtHeights = assignedAnnotations[RangeUtil.ToTensorIndex(..), 3] - assignedAnnotations[RangeUtil.ToTensorIndex(..), 1]; 130var gtCtrX = assignedAnnotations[RangeUtil.ToTensorIndex(..), 0] + (0.5 * gtWidths); 131var gtCtrY = assignedAnnotations[RangeUtil.ToTensorIndex(..), 1] + (0.5 * gtHeights); 181var area = (b[RangeUtil.ToTensorIndex(..), 2] - b[RangeUtil.ToTensorIndex(..), 0]) * (b[RangeUtil.ToTensorIndex(..), 3] - b[RangeUtil.ToTensorIndex(..), 1]); 183var iw = torch.minimum(input: torch.unsqueeze(a[RangeUtil.ToTensorIndex(..), 2], dim: 1), b[RangeUtil.ToTensorIndex(..), 2]) - 184torch.maximum(input: torch.unsqueeze(a[RangeUtil.ToTensorIndex(..), 0], 1), b[RangeUtil.ToTensorIndex(..), 0]); 185var ih = torch.minimum(input: torch.unsqueeze(a[RangeUtil.ToTensorIndex(..), 3], dim: 1), b[RangeUtil.ToTensorIndex(..), 3]) - 186torch.maximum(input: torch.unsqueeze(a[RangeUtil.ToTensorIndex(..), 1], 1), b[RangeUtil.ToTensorIndex(..), 1]); 191var ua = torch.unsqueeze((a[RangeUtil.ToTensorIndex(..), 2] - a[RangeUtil.ToTensorIndex(..), 0]) * (a[RangeUtil.ToTensorIndex(..), 3] - a[RangeUtil.ToTensorIndex(..), 1]), dim: 1) + area - (iw * ih);
Utils\ImageUtils.cs (40)
53var scores1 = torch.squeeze(classification[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), i], null); 111var x1 = boxes[RangeUtil.ToTensorIndex(..), 0]; 112var y1 = boxes[RangeUtil.ToTensorIndex(..), 1]; 113var x2 = boxes[RangeUtil.ToTensorIndex(..), 2]; 114var y2 = boxes[RangeUtil.ToTensorIndex(..), 3]; 120var order = _order[RangeUtil.ToTensorIndex(..)]; 136var xx1 = x1[order[RangeUtil.ToTensorIndex(1..)]].clamp(min: x1[i]); // [N - 1,] 137var yy1 = y1[order[RangeUtil.ToTensorIndex(1..)]].clamp(min: y1[i]); 138var xx2 = x2[order[RangeUtil.ToTensorIndex(1..)]].clamp(max: x2[i]); 139var yy2 = y2[order[RangeUtil.ToTensorIndex(1..)]].clamp(max: y2[i]); 142var iou = inter / (areas[i] + areas[order[RangeUtil.ToTensorIndex(1..)]] - inter); // [N-1, ] 170var widths = boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 2] - boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 0]; 171var heights = boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 3] - boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 1]; 172var ctrX = boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 0] + (0.5 * widths); 173var ctrY = boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 1] + (0.5 * heights); 175var dx = (deltas[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 0] * std[0]) + mean[0]; 176var dy = (deltas[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 1] * std[1]) + mean[1]; 177var dw = (deltas[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 2] * std[2]) + mean[2]; 178var dh = (deltas[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 3] * std[3]) + mean[3]; 213var clippedBoxesX0 = torch.clamp(boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 0], min: 0); 214var clippedBoxesY0 = torch.clamp(boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 1], min: 0); 216var clippedBoxesX1 = torch.clamp(boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 2], max: width); 217var clippedBoxesY1 = torch.clamp(boxes[RangeUtil.ToTensorIndex(..), RangeUtil.ToTensorIndex(..), 3], max: height);