def __call__(self, x, im_info, gpu=-1, gt_boxes=None):
n = x.data.shape[0]
h = F.relu(self.rpn_conv_3x3(x))
rpn_cls_score = self.rpn_cls_score(h)
rpn_bbox_pred = self.rpn_bbox_pred(h)
rpn_cls_score_reshape = F.reshape(rpn_cls_score, (n, 2, -1))
# RoI Proposal
rpn_cls_prob = F.softmax(rpn_cls_score_reshape)
c, hh, ww = rpn_cls_score.data.shape[1:]
rpn_cls_prob_reshape = F.reshape(rpn_cls_prob, (n, c, hh, ww))
rois = self.proposal_layer(
rpn_cls_prob_reshape, rpn_bbox_pred, im_info, gt_boxes is not None)
if gt_boxes is not None:
rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, \
rpn_bbox_outside_weights = self.anchor_target_layer(
rpn_cls_score, gt_boxes, im_info)
rpn_labels = rpn_labels.reshape((1, -1))
# make it into Variable
if gpu >= 0:
tg = lambda x: to_gpu(x, device=gpu)
rpn_labels = tg(rpn_labels)
rpn_bbox_targets = tg(rpn_bbox_targets)
rpn_bbox_inside_weights = tg(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = tg(rpn_bbox_outside_weights)
volatile = 'off' if gt_boxes is not None else 'on'
rpn_labels = Variable(rpn_labels, volatile=volatile)
rpn_bbox_targets = Variable(rpn_bbox_targets, volatile=volatile)
# rpn_bbox_inside_weights = Variable(
# rpn_bbox_inside_weights, volatile=volatile)
# rpn_bbox_outside_weights = Variable(
# rpn_bbox_outside_weights, volatile=volatile)
rpn_cls_loss = F.softmax_cross_entropy(
rpn_cls_score_reshape, rpn_labels)
rpn_loss_bbox = smooth_l1_loss(
rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, self.rpn_sigma)
return rpn_cls_loss, rpn_loss_bbox, rois
else:
return rois
def __call__(self, x, im_info, gpu=-1, gt_boxes=None):
n = x.data.shape[0]
h = F.relu(self.rpn_conv_3x3(x))
rpn_cls_score = self.rpn_cls_score(h)
rpn_bbox_pred = self.rpn_bbox_pred(h)
rpn_cls_score_reshape = F.reshape(rpn_cls_score, (n, 2, -1))
# RoI Proposal
rpn_cls_prob = F.softmax(rpn_cls_score_reshape)
c, hh, ww = rpn_cls_score.data.shape[1:]
rpn_cls_prob_reshape = F.reshape(rpn_cls_prob, (n, c, hh, ww))
rois = self.proposal_layer(
rpn_cls_prob_reshape, rpn_bbox_pred, im_info, gt_boxes is not None)
if gt_boxes is not None:
rpn_labels, rpn_bbox_targets, rpn_bbox_inside_weights, \
rpn_bbox_outside_weights = self.anchor_target_layer(
rpn_cls_score, gt_boxes, im_info)
rpn_labels = rpn_labels.reshape((1, -1))
# make it into Variable
if gpu >= 0:
tg = lambda x: to_gpu(x, device=gpu)
rpn_labels = tg(rpn_labels)
rpn_bbox_targets = tg(rpn_bbox_targets)
rpn_bbox_inside_weights = tg(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = tg(rpn_bbox_outside_weights)
rpn_labels = Variable(rpn_labels, volatile='auto')
rpn_bbox_targets = Variable(rpn_bbox_targets, volatile='auto')
# rpn_bbox_inside_weights = Variable(
# rpn_bbox_inside_weights, volatile=volatile)
# rpn_bbox_outside_weights = Variable(
# rpn_bbox_outside_weights, volatile=volatile)
rpn_cls_loss = F.softmax_cross_entropy(
rpn_cls_score_reshape, rpn_labels)
rpn_loss_bbox = smooth_l1_loss(
rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, self.rpn_sigma)
return rpn_cls_loss, rpn_loss_bbox, rois
else:
return rois
def __call__(self, x, im_info, gt_boxes=None):
h = self.trunk(x)
if isinstance(im_info, chainer.cuda.cupy.ndarray):
im_info = chainer.cuda.cupy.asnumpy(im_info)
if self.train:
im_info = im_info.data
gt_boxes = gt_boxes.data
if isinstance(gt_boxes, chainer.cuda.cupy.ndarray):
im_info = chainer.cuda.cupy.asnumpy(im_info)
gt_boxes = chainer.cuda.cupy.asnumpy(gt_boxes)
rpn_cls_loss, rpn_loss_bbox, rois = self.RPN(
h, im_info, self.gpu, gt_boxes)
else:
rois = self.RPN(h, im_info, self.gpu, gt_boxes)
if self.train:
rois, labels, bbox_targets, bbox_inside_weights, \
bbox_outside_weights = self.proposal_target_layer(
rois, gt_boxes)
# Convert rois
if self.gpu >= 0:
rois = to_gpu(rois, device=self.gpu)
im_info = to_gpu(im_info, device=self.gpu)
with chainer.cuda.Device(self.gpu):
boxes = rois[:, 1:5] / im_info[0][2]
else:
boxes = rois[:, 1:5] / im_info[0][2]
# RCNN
pool5 = roi_pooling_2d(
self.trunk.feature, rois, 7, 7, self.spatial_scale)
fc6 = F.dropout(F.relu(self.fc6(pool5)), train=self.train)
fc7 = F.dropout(F.relu(self.fc7(fc6)), train=self.train)
# Per class probability
cls_score = self.cls_score(fc7)
cls_prob = F.softmax(cls_score)
# BBox predictions
bbox_pred = self.bbox_pred(fc7)
box_deltas = bbox_pred.data
if self.train:
if self.gpu >= 0:
tg = lambda x: to_gpu(x, device=self.gpu)
labels = tg(labels)
bbox_targets = tg(bbox_targets)
bbox_inside_weights = tg(bbox_inside_weights)
bbox_outside_weights = tg(bbox_outside_weights)
loss_cls = F.softmax_cross_entropy(cls_score, labels)
labels = Variable(labels, volatile='off')
bbox_targets = Variable(bbox_targets, volatile='off')
loss_bbox = smooth_l1_loss(
bbox_pred, bbox_targets, bbox_inside_weights,
bbox_outside_weights, self.sigma)
reporter.report({'rpn_loss_cls': rpn_cls_loss,
'rpn_loss_bbox': rpn_loss_bbox,
'loss_bbox': loss_bbox,
'loss_cls': loss_cls}, self)
return rpn_cls_loss, rpn_loss_bbox, loss_bbox, loss_cls
else:
pred_boxes = bbox_transform_inv(boxes, box_deltas, self.gpu)
pred_boxes = clip_boxes(pred_boxes, im_info[0][:2], self.gpu)
return cls_prob, pred_boxes
def __call__(self, x, im_info, gt_boxes=None):
h = self.trunk(x)
if chainer.cuda.available \
and isinstance(im_info, chainer.cuda.cupy.ndarray):
im_info = chainer.cuda.cupy.asnumpy(im_info)
if self.train:
im_info = im_info.data
gt_boxes = gt_boxes.data
if isinstance(gt_boxes, chainer.cuda.cupy.ndarray):
im_info = chainer.cuda.cupy.asnumpy(im_info)
gt_boxes = chainer.cuda.cupy.asnumpy(gt_boxes)
rpn_cls_loss, rpn_loss_bbox, rois = self.RPN(
h, im_info, self.gpu, gt_boxes)
else:
rois = self.RPN(h, im_info, self.gpu, gt_boxes)
if self.train:
rois, labels, bbox_targets, bbox_inside_weights, \
bbox_outside_weights = self.proposal_target_layer(
rois, gt_boxes)
# Convert rois
if self.gpu >= 0:
rois = to_gpu(rois, device=self.gpu)
im_info = to_gpu(im_info, device=self.gpu)
with chainer.cuda.Device(self.gpu):
boxes = rois[:, 1:5] / im_info[0][2]
else:
boxes = rois[:, 1:5] / im_info[0][2]
# RCNN
pool5 = roi_pooling_2d(self.trunk.feature, rois, 7, 7,
self.spatial_scale)
fc6 = F.dropout(F.relu(self.fc6(pool5)), train=self.train)
fc7 = F.dropout(F.relu(self.fc7(fc6)), train=self.train)
# Per class probability
cls_score = self.cls_score(fc7)
cls_prob = F.softmax(cls_score)
# BBox predictions
bbox_pred = self.bbox_pred(fc7)
box_deltas = bbox_pred.data
if self.train:
if self.gpu >= 0:
tg = lambda x: to_gpu(x, device=self.gpu)
labels = tg(labels)
bbox_targets = tg(bbox_targets)
bbox_inside_weights = tg(bbox_inside_weights)
bbox_outside_weights = tg(bbox_outside_weights)
loss_cls = F.softmax_cross_entropy(cls_score, labels)
labels = Variable(labels, volatile='auto')
bbox_targets = Variable(bbox_targets, volatile='auto')
loss_bbox = smooth_l1_loss(bbox_pred, bbox_targets,
bbox_inside_weights,
bbox_outside_weights, self.sigma)
reporter.report(
{
'rpn_loss_cls': rpn_cls_loss,
'rpn_loss_bbox': rpn_loss_bbox,
'loss_bbox': loss_bbox,
'loss_cls': loss_cls
}, self)
return rpn_cls_loss, rpn_loss_bbox, loss_bbox, loss_cls
else:
pred_boxes = bbox_transform_inv(boxes, box_deltas, self.gpu)
pred_boxes = clip_boxes(pred_boxes, im_info[0][:2], self.gpu)
return cls_prob, pred_boxes