本文整理汇总了Python中model.utils.net_utils._smooth_l1_loss方法的典型用法代码示例。如果您正苦于以下问题:Python net_utils._smooth_l1_loss方法的具体用法?Python net_utils._smooth_l1_loss怎么用?Python net_utils._smooth_l1_loss使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类model.utils.net_utils
的用法示例。
在下文中一共展示了net_utils._smooth_l1_loss方法的12个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: detect_loss
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def detect_loss(self, cls_score, rois_label, bbox_pred, rois_target, rois_inside_ws, rois_outside_ws):
# classification loss
RCNN_loss_cls = F.cross_entropy(cls_score, rois_label)
# bounding box regression L1 loss
RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target, rois_inside_ws, rois_outside_ws)
return RCNN_loss_cls, RCNN_loss_bbox
示例2: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, dim=1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes[:,:,:5], im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例3: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target(
(rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3,
1).contiguous().view(
batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1, 2), 0,
rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
rpn_bbox_targets, rpn_bbox_inside_weights, \
rpn_bbox_outside_weights = rpn_data[
1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets,
rpn_bbox_inside_weights,
rpn_bbox_outside_weights,
sigma=3, dim=[1, 2, 3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例4: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:-1]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
if self.training:
return rois, self.rpn_loss_cls, self.rpn_loss_box, rpn_data[-1]
return rois, self.rpn_loss_cls, self.rpn_loss_box, None
示例5: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, dim=1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例6: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, dim=1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1, 2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1, 2, 3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例7: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例8: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例9: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, dim=1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
rpn_label = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1, 2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1, 2, 3])
return rois, self.rpn_loss_cls, self.rpn_loss_box, rpn_label, rpn_conv1, rpn_cls_score
示例10: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes, need_backprop=None):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
# print(rpn_cls_score_reshape.size())
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape,1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# print(rpn_bbox_pred.max())
# proposal layer
# cfg_key = 'TRAIN' if self.training else 'TEST'
cfg_key = 'TRAIN' if need_backprop.numpy() else 'TEST'
# cfg_key = 'TRAIN' if need_backprop.numpy() else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例11: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes, target=False):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training and not target else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key))
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training and not target:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box
示例12: forward
# 需要导入模块: from model.utils import net_utils [as 别名]
# 或者: from model.utils.net_utils import _smooth_l1_loss [as 别名]
def forward(self, base_feat, im_info, gt_boxes, num_boxes,target=False):
batch_size = base_feat.size(0)
# return feature map after convrelu layer
rpn_conv1 = F.relu(self.RPN_Conv(base_feat), inplace=True)
# get rpn classification score
rpn_cls_score = self.RPN_cls_score(rpn_conv1)
rpn_cls_score_reshape = self.reshape(rpn_cls_score, 2)
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape, 1)
rpn_cls_prob = self.reshape(rpn_cls_prob_reshape, self.nc_score_out)
# get rpn offsets to the anchor boxes
rpn_bbox_pred = self.RPN_bbox_pred(rpn_conv1)
# proposal layer
cfg_key = 'TRAIN' if self.training else 'TEST'
rois = self.RPN_proposal((rpn_cls_prob.data, rpn_bbox_pred.data,
im_info, cfg_key),target=target)
self.rpn_loss_cls = 0
self.rpn_loss_box = 0
# generating training labels and build the rpn loss
if self.training:
assert gt_boxes is not None
rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes))
# compute classification loss
rpn_cls_score = rpn_cls_score_reshape.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
rpn_label = rpn_data[0].view(batch_size, -1)
rpn_keep = Variable(rpn_label.view(-1).ne(-1).nonzero().view(-1))
rpn_cls_score = torch.index_select(rpn_cls_score.view(-1,2), 0, rpn_keep)
rpn_label = torch.index_select(rpn_label.view(-1), 0, rpn_keep.data)
rpn_label = Variable(rpn_label.long())
self.rpn_loss_cls = F.cross_entropy(rpn_cls_score, rpn_label)
fg_cnt = torch.sum(rpn_label.data.ne(0))
rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data[1:]
# compute bbox regression loss
rpn_bbox_inside_weights = Variable(rpn_bbox_inside_weights)
rpn_bbox_outside_weights = Variable(rpn_bbox_outside_weights)
rpn_bbox_targets = Variable(rpn_bbox_targets)
self.rpn_loss_box = _smooth_l1_loss(rpn_bbox_pred, rpn_bbox_targets, rpn_bbox_inside_weights,
rpn_bbox_outside_weights, sigma=3, dim=[1,2,3])
return rois, self.rpn_loss_cls, self.rpn_loss_box