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from opt import opt
try:
from utils.img import transformBoxInvert, transformBoxInvert_batch, findPeak, processPeaks
except ImportError:
from SPPE.src.utils.img import transformBoxInvert, transformBoxInvert_batch, findPeak, processPeaks
import torch
class DataLogger(object):
def __init__(self):
self.clear()
def clear(self):
self.value = 0
self.sum = 0
self.cnt = 0
self.avg = 0
def update(self, value, n=1):
self.value = value
self.sum += value * n
self.cnt += n
self._cal_avg()
def _cal_avg(self):
self.avg = self.sum / self.cnt
def accuracy(output, label, dataset):
if type(output) == list:
return accuracy(output[opt.nStack - 1], label[opt.nStack - 1], dataset)
else:
return heatmapAccuracy(output.cpu().data, label.cpu().data, dataset.accIdxs)
def heatmapAccuracy(output, label, idxs):
preds = getPreds(output)
gt = getPreds(label)
norm = torch.ones(preds.size(0)) * opt.outputResH / 10
dists = calc_dists(preds, gt, norm)
#print(dists)
acc = torch.zeros(len(idxs) + 1)
avg_acc = 0
cnt = 0
for i in range(len(idxs)):
acc[i + 1] = dist_acc(dists[idxs[i] - 1])
if acc[i + 1] >= 0:
avg_acc = avg_acc + acc[i + 1]
cnt += 1
if cnt != 0:
acc[0] = avg_acc / cnt
return acc
def getPreds(hm):
''' get predictions from score maps in torch Tensor
return type: torch.LongTensor
'''
assert hm.dim() == 4, 'Score maps should be 4-dim'
maxval, idx = torch.max(hm.view(hm.size(0), hm.size(1), -1), 2)
maxval = maxval.view(hm.size(0), hm.size(1), 1)
idx = idx.view(hm.size(0), hm.size(1), 1) + 1
preds = idx.repeat(1, 1, 2).float()
preds[:, :, 0] = (preds[:, :, 0] - 1) % hm.size(3)
preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hm.size(3))
# pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
# preds *= pred_mask
return preds
def calc_dists(preds, target, normalize):
preds = preds.float().clone()
target = target.float().clone()
dists = torch.zeros(preds.size(1), preds.size(0))
for n in range(preds.size(0)):
for c in range(preds.size(1)):
if target[n, c, 0] > 0 and target[n, c, 1] > 0:
dists[c, n] = torch.dist(
preds[n, c, :], target[n, c, :]) / normalize[n]
else:
dists[c, n] = -1
return dists
def dist_acc(dists, thr=0.5):
''' Return percentage below threshold while ignoring values with a -1 '''
if dists.ne(-1).sum() > 0:
return dists.le(thr).eq(dists.ne(-1)).float().sum() * 1.0 / dists.ne(-1).float().sum()
else:
return - 1
def postprocess(output):
p = getPreds(output)
for i in range(p.size(0)):
for j in range(p.size(1)):
hm = output[i][j]
pX, pY = int(round(p[i][j][0])), int(round(p[i][j][1]))
if 0 < pX < opt.outputResW - 1 and 0 < pY < opt.outputResH - 1:
diff = torch.Tensor((hm[pY][pX + 1] - hm[pY][pX - 1], hm[pY + 1][pX] - hm[pY - 1][pX]))
p[i][j] += diff.sign() * 0.25
p -= 0.5
return p
def getPrediction(hms, pt1, pt2, inpH, inpW, resH, resW):
'''
Get keypoint location from heatmaps
'''
assert hms.dim() == 4, 'Score maps should be 4-dim'
maxval, idx = torch.max(hms.view(hms.size(0), hms.size(1), -1), 2)
maxval = maxval.view(hms.size(0), hms.size(1), 1)
idx = idx.view(hms.size(0), hms.size(1), 1) + 1
preds = idx.repeat(1, 1, 2).float()
preds[:, :, 0] = (preds[:, :, 0] - 1) % hms.size(3)
preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hms.size(3))
pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
preds *= pred_mask
# Very simple post-processing step to improve performance at tight PCK thresholds
for i in range(preds.size(0)):
for j in range(preds.size(1)):
hm = hms[i][j]
pX, pY = int(round(float(preds[i][j][0]))), int(round(float(preds[i][j][1])))
if 0 < pX < opt.outputResW - 1 and 0 < pY < opt.outputResH - 1:
diff = torch.Tensor(
(hm[pY][pX + 1] - hm[pY][pX - 1], hm[pY + 1][pX] - hm[pY - 1][pX]))
preds[i][j] += diff.sign() * 0.25
preds += 0.2
preds_tf = torch.zeros(preds.size())
preds_tf = transformBoxInvert_batch(preds, pt1, pt2, inpH, inpW, resH, resW)
return preds, preds_tf, maxval
def getMultiPeakPrediction(hms, pt1, pt2, inpH, inpW, resH, resW):
assert hms.dim() == 4, 'Score maps should be 4-dim'
preds_img = {}
hms = hms.numpy()
for n in range(hms.shape[0]): # Number of samples
preds_img[n] = {} # Result of sample: n
for k in range(hms.shape[1]): # Number of keypoints
preds_img[n][k] = [] # Result of keypoint: k
hm = hms[n][k]
candidate_points = findPeak(hm)
res_pt = processPeaks(candidate_points, hm,
pt1[n], pt2[n], inpH, inpW, resH, resW)
preds_img[n][k] = res_pt
return preds_img
def getPrediction_batch(hms, pt1, pt2, inpH, inpW, resH, resW):
'''
Get keypoint location from heatmaps
pt1, pt2: [n, 2]
OUTPUT:
preds: [n, 17, 2]
'''
assert hms.dim() == 4, 'Score maps should be 4-dim'
flat_hms = hms.view(hms.size(0), hms.size(1), -1)
maxval, idx = torch.max(flat_hms, 2)
maxval = maxval.view(hms.size(0), hms.size(1), 1)
idx = idx.view(hms.size(0), hms.size(1), 1) + 1
preds = idx.repeat(1, 1, 2).float()
preds[:, :, 0] = (preds[:, :, 0] - 1) % hms.size(3)
preds[:, :, 1] = torch.floor((preds[:, :, 1] - 1) / hms.size(3))
pred_mask = maxval.gt(0).repeat(1, 1, 2).float()
preds *= pred_mask
# Very simple post-processing step to improve performance at tight PCK thresholds
idx_up = (idx - hms.size(3)).clamp(0, flat_hms.size(2) - 1)
idx_down = (idx + hms.size(3)).clamp(0, flat_hms.size(2) - 1)
idx_left = (idx - 1).clamp(0, flat_hms.size(2) - 1)
idx_right = (idx + 1).clamp(0, flat_hms.size(2) - 1)
maxval_up = flat_hms.gather(2, idx_up)
maxval_down = flat_hms.gather(2, idx_down)
maxval_left = flat_hms.gather(2, idx_left)
maxval_right = flat_hms.gather(2, idx_right)
diff1 = (maxval_right - maxval_left).sign() * 0.25
diff2 = (maxval_down - maxval_up).sign() * 0.25
diff1[idx_up <= hms.size(3)] = 0
diff1[idx_down / hms.size(3) >= (hms.size(3) - 1)] = 0
diff2[(idx_left % hms.size(3)) == 0] = 0
diff2[(idx_left % hms.size(3)) == (hms.size(3) - 1)] = 0
preds[:, :, 0] += diff1.squeeze(-1)
preds[:, :, 1] += diff2.squeeze(-1)
preds_tf = torch.zeros(preds.size())
preds_tf = transformBoxInvert_batch(preds, pt1, pt2, inpH, inpW, resH, resW)
return preds, preds_tf, maxval
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