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- import numpy as np
- from itertools import product as product
- import torch
- from torch.autograd import Function
- def nms_(dets, thresh):
- """
- Courtesy of Ross Girshick
- [https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py]
- """
- x1 = dets[:, 0]
- y1 = dets[:, 1]
- x2 = dets[:, 2]
- y2 = dets[:, 3]
- scores = dets[:, 4]
- areas = (x2 - x1) * (y2 - y1)
- order = scores.argsort()[::-1]
- keep = []
- while order.size > 0:
- i = order[0]
- keep.append(int(i))
- xx1 = np.maximum(x1[i], x1[order[1:]])
- yy1 = np.maximum(y1[i], y1[order[1:]])
- xx2 = np.minimum(x2[i], x2[order[1:]])
- yy2 = np.minimum(y2[i], y2[order[1:]])
- w = np.maximum(0.0, xx2 - xx1)
- h = np.maximum(0.0, yy2 - yy1)
- inter = w * h
- ovr = inter / (areas[i] + areas[order[1:]] - inter)
- inds = np.where(ovr <= thresh)[0]
- order = order[inds + 1]
- return np.array(keep).astype(np.int)
- def decode(loc, priors, variances):
- """Decode locations from predictions using priors to undo
- the encoding we did for offset regression at train time.
- Args:
- loc (tensor): location predictions for loc layers,
- Shape: [num_priors,4]
- priors (tensor): Prior boxes in center-offset form.
- Shape: [num_priors,4].
- variances: (list[float]) Variances of priorboxes
- Return:
- decoded bounding box predictions
- """
- boxes = torch.cat((
- priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
- priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
- boxes[:, :2] -= boxes[:, 2:] / 2
- boxes[:, 2:] += boxes[:, :2]
- return boxes
- def nms(boxes, scores, overlap=0.5, top_k=200):
- """Apply non-maximum suppression at test time to avoid detecting too many
- overlapping bounding boxes for a given object.
- Args:
- boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
- scores: (tensor) The class predscores for the img, Shape:[num_priors].
- overlap: (float) The overlap thresh for suppressing unnecessary boxes.
- top_k: (int) The Maximum number of box preds to consider.
- Return:
- The indices of the kept boxes with respect to num_priors.
- """
- keep = scores.new(scores.size(0)).zero_().long()
- if boxes.numel() == 0:
- return keep, 0
- x1 = boxes[:, 0]
- y1 = boxes[:, 1]
- x2 = boxes[:, 2]
- y2 = boxes[:, 3]
- area = torch.mul(x2 - x1, y2 - y1)
- v, idx = scores.sort(0) # sort in ascending order
- # I = I[v >= 0.01]
- idx = idx[-top_k:] # indices of the top-k largest vals
- xx1 = boxes.new()
- yy1 = boxes.new()
- xx2 = boxes.new()
- yy2 = boxes.new()
- w = boxes.new()
- h = boxes.new()
- # keep = torch.Tensor()
- count = 0
- while idx.numel() > 0:
- i = idx[-1] # index of current largest val
- # keep.append(i)
- keep[count] = i
- count += 1
- if idx.size(0) == 1:
- break
- idx = idx[:-1] # remove kept element from view
- # load bboxes of next highest vals
- torch.index_select(x1, 0, idx, out=xx1)
- torch.index_select(y1, 0, idx, out=yy1)
- torch.index_select(x2, 0, idx, out=xx2)
- torch.index_select(y2, 0, idx, out=yy2)
- # store element-wise max with next highest score
- xx1 = torch.clamp(xx1, min=x1[i])
- yy1 = torch.clamp(yy1, min=y1[i])
- xx2 = torch.clamp(xx2, max=x2[i])
- yy2 = torch.clamp(yy2, max=y2[i])
- w.resize_as_(xx2)
- h.resize_as_(yy2)
- w = xx2 - xx1
- h = yy2 - yy1
- # check sizes of xx1 and xx2.. after each iteration
- w = torch.clamp(w, min=0.0)
- h = torch.clamp(h, min=0.0)
- inter = w * h
- # IoU = i / (area(a) + area(b) - i)
- rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
- union = (rem_areas - inter) + area[i]
- IoU = inter / union # store result in iou
- # keep only elements with an IoU <= overlap
- idx = idx[IoU.le(overlap)]
- return keep, count
- class Detect(object):
- def __init__(self, num_classes=2,
- top_k=750, nms_thresh=0.3, conf_thresh=0.05,
- variance=[0.1, 0.2], nms_top_k=5000):
-
- self.num_classes = num_classes
- self.top_k = top_k
- self.nms_thresh = nms_thresh
- self.conf_thresh = conf_thresh
- self.variance = variance
- self.nms_top_k = nms_top_k
- def forward(self, loc_data, conf_data, prior_data):
- num = loc_data.size(0)
- num_priors = prior_data.size(0)
- conf_preds = conf_data.view(num, num_priors, self.num_classes).transpose(2, 1)
- batch_priors = prior_data.view(-1, num_priors, 4).expand(num, num_priors, 4)
- batch_priors = batch_priors.contiguous().view(-1, 4)
- decoded_boxes = decode(loc_data.view(-1, 4), batch_priors, self.variance)
- decoded_boxes = decoded_boxes.view(num, num_priors, 4)
- output = torch.zeros(num, self.num_classes, self.top_k, 5)
- for i in range(num):
- boxes = decoded_boxes[i].clone()
- conf_scores = conf_preds[i].clone()
- for cl in range(1, self.num_classes):
- c_mask = conf_scores[cl].gt(self.conf_thresh)
- scores = conf_scores[cl][c_mask]
-
- if scores.dim() == 0:
- continue
- l_mask = c_mask.unsqueeze(1).expand_as(boxes)
- boxes_ = boxes[l_mask].view(-1, 4)
- ids, count = nms(boxes_, scores, self.nms_thresh, self.nms_top_k)
- count = count if count < self.top_k else self.top_k
- output[i, cl, :count] = torch.cat((scores[ids[:count]].unsqueeze(1), boxes_[ids[:count]]), 1)
- return output
- class PriorBox(object):
- def __init__(self, input_size, feature_maps,
- variance=[0.1, 0.2],
- min_sizes=[16, 32, 64, 128, 256, 512],
- steps=[4, 8, 16, 32, 64, 128],
- clip=False):
- super(PriorBox, self).__init__()
- self.imh = input_size[0]
- self.imw = input_size[1]
- self.feature_maps = feature_maps
- self.variance = variance
- self.min_sizes = min_sizes
- self.steps = steps
- self.clip = clip
- def forward(self):
- mean = []
- for k, fmap in enumerate(self.feature_maps):
- feath = fmap[0]
- featw = fmap[1]
- for i, j in product(range(feath), range(featw)):
- f_kw = self.imw / self.steps[k]
- f_kh = self.imh / self.steps[k]
- cx = (j + 0.5) / f_kw
- cy = (i + 0.5) / f_kh
- s_kw = self.min_sizes[k] / self.imw
- s_kh = self.min_sizes[k] / self.imh
- mean += [cx, cy, s_kw, s_kh]
- output = torch.FloatTensor(mean).view(-1, 4)
-
- if self.clip:
- output.clamp_(max=1, min=0)
-
- return output
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