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region_loss.py
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region_loss.py
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import time
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from utils import *
def build_targets(pred_corners, target, num_keypoints, num_anchors, num_classes, nH, nW, noobject_scale, object_scale, sil_thresh, seen):
nB = target.size(0)
nA = num_anchors
nC = num_classes
conf_mask = torch.ones(nB, nA, nH, nW) * noobject_scale
coord_mask = torch.zeros(nB, nA, nH, nW)
cls_mask = torch.zeros(nB, nA, nH, nW)
txs = list()
tys = list()
for i in range(num_keypoints):
txs.append(torch.zeros(nB, nA, nH, nW))
tys.append(torch.zeros(nB, nA, nH, nW))
tconf = torch.zeros(nB, nA, nH, nW)
tcls = torch.zeros(nB, nA, nH, nW)
num_labels = 2 * num_keypoints + 3 # +2 for width, height and +1 for class within label files
nAnchors = nA*nH*nW
nPixels = nH*nW
for b in range(nB):
cur_pred_corners = pred_corners[b*nAnchors:(b+1)*nAnchors].t()
cur_confs = torch.zeros(nAnchors)
for t in range(50):
if target[b][t*num_labels+1] == 0:
break
g = list()
for i in range(num_keypoints):
g.append(target[b][t*num_labels+2*i+1])
g.append(target[b][t*num_labels+2*i+2])
cur_gt_corners = torch.FloatTensor(g).repeat(nAnchors,1).t() # 16 x nAnchors
cur_confs = torch.max(cur_confs, corner_confidences(cur_pred_corners, cur_gt_corners)).view_as(conf_mask[b]) # some irrelevant areas are filtered, in the same grid multiple anchor boxes might exceed the threshold
conf_mask[b][cur_confs>sil_thresh] = 0
nGT = 0
nCorrect = 0
for b in range(nB):
for t in range(50):
if target[b][t*num_labels+1] == 0:
break
# Get gt box for the current label
nGT = nGT + 1
gx = list()
gy = list()
gt_box = list()
for i in range(num_keypoints):
gt_box.extend([target[b][t*num_labels+2*i+1], target[b][t*num_labels+2*i+2]])
gx.append(target[b][t*num_labels+2*i+1] * nW)
gy.append(target[b][t*num_labels+2*i+2] * nH)
if i == 0:
gi0 = int(gx[i])
gj0 = int(gy[i])
# Update masks
best_n = 0 # 1 anchor box
pred_box = pred_corners[b*nAnchors+best_n*nPixels+gj0*nW+gi0]
conf = corner_confidence(gt_box, pred_box)
coord_mask[b][best_n][gj0][gi0] = 1
cls_mask[b][best_n][gj0][gi0] = 1
conf_mask[b][best_n][gj0][gi0] = object_scale
# Update targets
for i in range(num_keypoints):
txs[i][b][best_n][gj0][gi0] = gx[i]- gi0
tys[i][b][best_n][gj0][gi0] = gy[i]- gj0
tconf[b][best_n][gj0][gi0] = conf
tcls[b][best_n][gj0][gi0] = target[b][t*num_labels]
# Update recall during training
if conf > 0.5:
nCorrect = nCorrect + 1
return nGT, nCorrect, coord_mask, conf_mask, cls_mask, txs, tys, tconf, tcls
class RegionLoss(nn.Module):
def __init__(self, num_keypoints=9, num_classes=1, anchors=[], num_anchors=1, pretrain_num_epochs=15):
# Define the loss layer
super(RegionLoss, self).__init__()
self.num_classes = num_classes
self.num_anchors = num_anchors # for single object pose estimation, there is only 1 trivial predictor (anchor)
self.num_keypoints = num_keypoints
self.coord_scale = 1
self.noobject_scale = 1
self.object_scale = 5
self.class_scale = 1
self.thresh = 0.6
self.seen = 0
self.pretrain_num_epochs = pretrain_num_epochs
def forward(self, output, target, epoch):
# Parameters
t0 = time.time()
nB = output.data.size(0)
nA = self.num_anchors
nC = self.num_classes
nH = output.data.size(2)
nW = output.data.size(3)
num_keypoints = self.num_keypoints
# Activation
output = output.view(nB, nA, (num_keypoints*2+1+nC), nH, nW)
x = list()
y = list()
x.append(torch.sigmoid(output.index_select(2, Variable(torch.cuda.LongTensor([0]))).view(nB, nA, nH, nW)))
y.append(torch.sigmoid(output.index_select(2, Variable(torch.cuda.LongTensor([1]))).view(nB, nA, nH, nW)))
for i in range(1,num_keypoints):
x.append(output.index_select(2, Variable(torch.cuda.LongTensor([2 * i + 0]))).view(nB, nA, nH, nW))
y.append(output.index_select(2, Variable(torch.cuda.LongTensor([2 * i + 1]))).view(nB, nA, nH, nW))
conf = torch.sigmoid(output.index_select(2, Variable(torch.cuda.LongTensor([2 * num_keypoints]))).view(nB, nA, nH, nW))
cls = output.index_select(2, Variable(torch.linspace(2*num_keypoints+1,2*num_keypoints+1+nC-1,nC).long().cuda()))
cls = cls.view(nB*nA, nC, nH*nW).transpose(1,2).contiguous().view(nB*nA*nH*nW, nC)
t1 = time.time()
# Create pred boxes
pred_corners = torch.cuda.FloatTensor(2*num_keypoints, nB*nA*nH*nW)
grid_x = torch.linspace(0, nW-1, nW).repeat(nH,1).repeat(nB*nA, 1, 1).view(nB*nA*nH*nW).cuda()
grid_y = torch.linspace(0, nH-1, nH).repeat(nW,1).t().repeat(nB*nA, 1, 1).view(nB*nA*nH*nW).cuda()
for i in range(num_keypoints):
pred_corners[2 * i + 0] = (x[i].data.view_as(grid_x) + grid_x) / nW
pred_corners[2 * i + 1] = (y[i].data.view_as(grid_y) + grid_y) / nH
gpu_matrix = pred_corners.transpose(0,1).contiguous().view(-1,2*num_keypoints)
pred_corners = convert2cpu(gpu_matrix)
t2 = time.time()
# Build targets
nGT, nCorrect, coord_mask, conf_mask, cls_mask, txs, tys, tconf, tcls = \
build_targets(pred_corners, target.data, num_keypoints, nA, nC, nH, nW, self.noobject_scale, self.object_scale, self.thresh, self.seen)
cls_mask = (cls_mask == 1)
nProposals = int((conf > 0.25).sum().data[0])
for i in range(num_keypoints):
txs[i] = Variable(txs[i].cuda())
tys[i] = Variable(tys[i].cuda())
tconf = Variable(tconf.cuda())
tcls = Variable(tcls[cls_mask].long().cuda())
coord_mask = Variable(coord_mask.cuda())
conf_mask = Variable(conf_mask.cuda().sqrt())
cls_mask = Variable(cls_mask.view(-1, 1).repeat(1,nC).cuda())
cls = cls[cls_mask].view(-1, nC)
t3 = time.time()
# Create loss
loss_xs = list()
loss_ys = list()
for i in range(num_keypoints):
loss_xs.append(self.coord_scale * nn.MSELoss(size_average=False)(x[i]*coord_mask, txs[i]*coord_mask)/2.0)
loss_ys.append(self.coord_scale * nn.MSELoss(size_average=False)(y[i]*coord_mask, tys[i]*coord_mask)/2.0)
loss_conf = nn.MSELoss(size_average=False)(conf*conf_mask, tconf*conf_mask)/2.0
loss_x = np.sum(loss_xs)
loss_y = np.sum(loss_ys)
if epoch > self.pretrain_num_epochs:
loss = loss_x + loss_y + loss_conf # in single object pose estimation, there is no classification loss
else:
# pretrain initially without confidence loss
# once the coordinate predictions get better, start training for confidence as well
loss = loss_x + loss_y
t4 = time.time()
if False:
print('-----------------------------------')
print(' activation : %f' % (t1 - t0))
print(' create pred_corners : %f' % (t2 - t1))
print(' build targets : %f' % (t3 - t2))
print(' create loss : %f' % (t4 - t3))
print(' total : %f' % (t4 - t0))
print('%d: nGT %d, recall %d, proposals %d, loss: x %f, y %f, conf %f, total %f' % (self.seen, nGT, nCorrect, nProposals, loss_x.data[0], loss_y.data[0], loss_conf.data[0], loss.data[0]))
return loss