def forward(self, x, alpha):
# (x has shape (batch_size, 3, h, w))
h = x.size()[2]
w = x.size()[3]
feature_map = self.network.get_feature(
x
) # (shape: (batch_size, 512, h/16, w/16)) (assuming self.resnet is ResNet18_OS16 or ResNet34_OS16. If self.resnet is ResNet18_OS8 or ResNet34_OS8, it will be (batch_size, 512, h/8, w/8). If self.resnet is ResNet50-152, it will be (batch_size, 4*512, h/16, w/16))
output = self.network(
x) # (shape: (batch_size, num_classes, h/16, w/16))
reverse_feature = ReverseLayerF.apply(feature_map, alpha)
print(reverse_feature.shape)
temp = self.conv1(reverse_feature)
#
# Your code here
print(temp.shape)
temp = F.max_pool2d(temp, (7, 7))
print(temp.shape)
temp = temp.view(temp.size(0), -1)
print(temp.shape)
temp = self.fc(temp)
print(temp.shape)
temp = F.log_softmax(temp, dim=1)
print(temp.shape)
return output, temp
def forward(self, noise, att, alpha):
h = torch.cat((noise, att), 1)
h1 = self.lrelu(self.fc1(h))
h2 = self.relu(self.fc2(h1))
feature = h2
reverse_feature = ReverseLayerF.apply(feature, alpha)
return feature, reverse_feature
def forward(self, x, alpha):
# print("x before model shape:"+str(x.shape))
out16 = self.in_tr(x)
out32 = self.down_tr32(out16)
out64 = self.down_tr64(out32)
out128 = self.down_tr128(out64)
# print("out16(in_tr) shape:"+str(out16.shape))
# print("out32(down_tr32) shape:"+str(out32.shape))
# print("out64(down_tr64) shape:"+str(out64.shape))
# print("out128(down_tr128) shape:"+str(out128.shape))
out256 = self.down_tr256(out128)
# print("out256(down_tr256) shape:"+str(out256.shape))
feature = out256.view(-1, 256 * 4 * 4 * 8)
reverse_feature = ReverseLayerF.apply(feature, alpha)
domain_output = self.domain_classifier(reverse_feature)
out = self.up_tr256(out256, out128)
# print("up_tr256 shape:"+str(out.shape))
out = self.up_tr128(out, out64)
# print("up_tr128 shape:"+str(out.shape))
out = self.up_tr64(out, out32)
# print("up_tr64 shape:"+str(out.shape))
out = self.up_tr32(out, out16)
# print("up_tr32 shape:"+str(out.shape))
out = self.out_tr(out)
# print("out_tr shape:"+str(out.shape))
return out, domain_output
def forward(self, x, alpha):
classify = self.fc1(x)
rev = ReverseLayerF.apply(x, alpha)
d = self.fc2(rev)
d = self.bn1(d)
d = F.relu(d)
discriminate = self.fc3(d)
return classify, discriminate
def forward(self, input_data, alpha):
input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)
feature = self.feature(input_data)
feature = feature.view(-1, 50 * 4 * 4)
reverse_feature = ReverseLayerF.apply(feature, alpha)
domain_output = self.domain_classifier(reverse_feature)
return domain_output
def dann(self, input_data, alpha):
feature = self.feature(input_data)
feature = feature.view(feature.size(0), -1)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output, feature
def forward(self, input_data, alpha):
feature = self.feature(input_data)
feature = feature.view(-1, 120)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output
def forward(self, input_data, alpha):
input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)
feature = self.feature(input_data)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output
def forward(self, input_data, alpha):
input_data = input_data.float()
feature = self.feature(input_data)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output
def forward(self, input_data, mode, rec_scheme, p=0.0):
result = []
if mode == 'source':
# source private encoder
private_feat = self.source_encoder_conv(input_data)
#print('source private feature shape before fc:', private_feat.shape)
private_feat = private_feat.view(-1, 64 * 7 * 7)
private_code = self.source_encoder_fc(private_feat)
#print('source private feature shape:', private_code.shape)
elif mode == 'target':
# target private encoder
private_feat = self.target_encoder_conv(input_data)
private_feat = private_feat.view(-1, 64 * 7 * 7)
private_code = self.target_encoder_fc(private_feat)
result.append(private_code)
# shared encoder
shared_feat = self.shared_encoder_conv(input_data)
#print('shared encoder feature shape before fc', shared_feat.shape)
shared_feat = shared_feat.view(-1, 48 * 7 * 7)
shared_code = self.shared_encoder_fc(shared_feat)
#print('shared encoder feature shape', shared_code.shape)
result.append(shared_code)
reversed_shared_code = ReverseLayerF.apply(shared_code, p)
domain_label = self.shared_encoder_pred_domain(reversed_shared_code)
result.append(domain_label)
if mode == 'source':
class_label = self.shared_encoder_pred_class(shared_code)
#print('source class label', class_label.shape)
result.append(class_label)
# shared decoder
if rec_scheme == 'share':
union_code = shared_code
elif rec_scheme == 'all':
union_code = private_code + shared_code
elif rec_scheme == 'private':
union_code = private_code
rec_vec = self.shared_decoder_fc(union_code)
#print('rec decoder feature shape before cov', rec_vec.shape)
rec_vec = rec_vec.view(-1, 3, 14, 14)
rec_code = self.shared_decoder_conv(rec_vec)
#print('rec decoder feature shape', rec_code.shape)
result.append(rec_code)
return result
def forward(self, input, cst):
input = input.expand(input.data.shape[0], 3, 28, 28)
attr = self.attr(input)
attr = attr.view(-1, 50 * 4 * 4)
reverse_attr = ReverseLayerF.apply(attr, cst)
class_output = self.cclass(attr)
domain_output = self.domain(reverse_attr)
# return class_output, domain_output
return class_output, domain_output, attr
def forward(self, x, alpha):
# import pdb; pdb.set_trace()
batch_size = len(x)
fe_out = self.fe(x)
fe_out = fe_out.view(batch_size, -1)
reverse_fe = ReverseLayerF.apply(fe_out, alpha)
label_out = self.label_classifier(fe_out)
domain_out = self.domain_classifier(reverse_fe)
return label_out, domain_out
def forward(self, input_data, alpha):
input_data = input_data.view(input_data.data.shape[0], 1,
self.max_length, self.input_dim)
feature = self.feature(input_data)
feature = feature.view(-1, 30 * 5 * 5)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output
def forward(self, input_data, mode, rec_scheme, p=0.0):
result = []
if mode == 'source':
# source private encoder
private_feat = self.source_encoder_conv(input_data)
private_feat = private_feat.view(-1, 64 * 7 * 7)
private_code = self.source_encoder_fc(private_feat)
elif mode == 'target':
# target private encoder
private_feat = self.target_encoder_conv(input_data)
private_feat = private_feat.view(-1, 64 * 7 * 7)
private_code = self.target_encoder_fc(private_feat)
result.append(private_code)
# shared encoder
shared_feat = self.shared_encoder_conv(input_data)
shared_feat = shared_feat.view(-1, 48 * 7 * 7)
shared_code = self.shared_encoder_fc(shared_feat)
result.append(shared_code)
reversed_shared_code = ReverseLayerF.apply(shared_code, p)
domain_label = self.shared_encoder_pred_domain(reversed_shared_code)
result.append(domain_label)
if mode == 'source':
class_label = self.shared_encoder_pred_class(shared_code)
result.append(class_label)
# if we add some target labeled data in training dataset
if mode == 'target':
class_label = self.shared_encoder_pred_class(shared_code)
result.append(class_label)
# shared decoder
if rec_scheme == 'share':
union_code = shared_code
elif rec_scheme == 'all':
union_code = private_code + shared_code
elif rec_scheme == 'private':
union_code = private_code
rec_vec = self.shared_decoder_fc(union_code)
rec_vec = rec_vec.view(-1, 3, 14, 14)
rec_code = self.shared_decoder_conv(rec_vec)
result.append(rec_code)
return result
def forward(self, x, alpha):
x = ReverseLayerF.apply(x, alpha)
x = self.ad_layer1(x)
x = self.relu1(x)
x = self.dropout1(x)
x = self.ad_layer2(x)
x = self.relu2(x)
x = self.dropout2(x)
x = self.ad_layer3(x)
x = self.sigmoid(x)
return x
def forward(self, input_data, alpha):
feature4096 = self.feature(input_data)
feature = self.bottleneck(feature4096)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output_st = self.dc_ip3_st(
self.domain_classifier(reverse_feature))
domain_output_mt = self.dc_ip3_mt(
self.domain_classifier(reverse_feature))
return class_output, domain_output_st, domain_output_mt, feature4096
def forward(self, x, alpha):
x = x.to(self.device)
x = x.squeeze().transpose(0, 1)
kld_loss, nll_loss, (all_enc_mean,
all_enc_std), (all_dec_mean,
all_dec_std), x1, h = self.vrnn(x)
reverse_feature = ReverseLayerF.apply(h.squeeze(), alpha)
regressor_output = self.regressor(reverse_feature)
domain_class_output = self.domain_classifier(reverse_feature)
return regressor_output, domain_class_output, kld_loss, nll_loss
def forward(self, input_data, alpha, phase, class_out):
if phase == 'train' and class_out:
# input_data = input_data.expand(input_data.data.shape[0], 3 ,400, 400)
# feature = self.feature(input_data)
# # print(feature.shape)
input_data = input_data.view(-1, 512)
# # print(feature.shape)
reverse_feature = ReverseLayerF.apply(input_data, alpha)
# print(reverse_feature.shape)
class_output = self.class_classifier(input_data)
# print(class_output.shape)
domain_output = self.domain_classifier(reverse_feature)
domain_output_class = self.domain_classifier_1(reverse_feature)
# print(class_output.shape)
# print(domain_output)
return class_output, domain_output, domain_output_class
if phase == 'train' and not class_out:
input_data = input_data.view(-1, 512)
# # print(feature.shape)
reverse_feature = ReverseLayerF.apply(input_data, alpha)
# print(reverse_feature.shape)
class_output = self.class_classifier(input_data)
# print(class_output.shape)
domain_output = self.domain_classifier(reverse_feature)
domain_output_class = self.domain_classifier_0(reverse_feature)
# print(class_output.shape)
# print(domain_output)
return class_output, domain_output, domain_output_class
else:
input_data = input_data.view(-1, 512 * 1 * 1)
class_output = self.class_classifier(input_data)
return class_output
def forward(self, input_data1, input_data2, alpha):
z1 = self.encode1(input_data1)
z2 = self.encode2(input_data2)
reconstructed = self.decode(z2)
z = torch.cat([z1, z2], axis=1)
reverse_z = ReverseLayerF.apply(z, alpha)
class_output = self.sentiment_classifier(z)
domain_output = self.domain_classifier(reverse_z)
return reconstructed, class_output, domain_output
def forward(self, input_data, alpha):
input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)
feature = self.feature(input_data)
feature = feature.view(-1, 50 * 4 * 4)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
# return class_output, domain_output
return class_output, domain_output, feature
# dann = CNNModel()
# print(dann)
def forward(self, input_data, alpha):
#print ("Input data before", input_data.shape)
input_data = input_data.expand(input_data.data.shape[0], 1,
input_data.data.shape[3],
input_data.data.shape[3])
#print ("After expand", input_data.shape)
feature = self.feature(input_data)
#print ("After feature", feature.shape)
feature = feature.view(-1, 50 * 13 * 13)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output
def forward(self, source, target, s_label, DEV, alpha=0.0):
source_share = self.sharedNet(source)
source_share = self.bottleneck(source_share)
source = self.source_fc(source_share)
p_source = self.softmax(source)
target = self.sharedNet(target)
target = self.bottleneck(target)
t_label = self.source_fc(target)
p_target = self.softmax(t_label)
t_label = t_label.data.max(1)[1]
s_out = []
t_out = []
if self.training == True:
# RevGrad
s_reverse_feature = ReverseLayerF.apply(source_share, alpha)
t_reverse_feature = ReverseLayerF.apply(target, alpha)
s_domain_output = self.domain_classifier(s_reverse_feature)
t_domain_output = self.domain_classifier(t_reverse_feature)
# p*feature-> classifier_i ->loss_i
for i in range(self.classes):
ps = p_source[:, i].reshape((target.shape[0],1))
fs = ps * s_reverse_feature
pt = p_target[:, i].reshape((target.shape[0],1))
ft = pt * t_reverse_feature
outsi = self.domain_classifiers[i](fs.cpu()).to(DEV)
s_out.append(outsi)
outti = self.domain_classifiers[i](ft.cpu()).to(DEV)
t_out.append(outti)
else:
s_domain_output = 0
t_domain_output = 0
s_out = [0]*self.classes
t_out = [0]*self.classes
return source, s_domain_output, t_domain_output, s_out, t_out
def forward(self, input_data, alpha, return_ddc_features=None):
if return_ddc_features is not None:
assert return_ddc_features in self.class_classifier._modules
feature = self.feature(input_data)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = feature
ddc_features = None
for k, v in self.class_classifier._modules.items():
class_output = v(class_output)
if k == return_ddc_features:
ddc_features = class_output
domain_output = self.domain_classifier(reverse_feature)
if return_ddc_features:
return class_output, domain_output, ddc_features
else:
return class_output, domain_output
def forward(self, input_data, alpha):
# input_data = input_data.expand(input_data.data.shape[0], 3 ,400, 400)
# input_data=self.main(input_data)
# # print(feature.shape)
input_data = input_data.view(-1, 512 * 7 * 7)
# # print(feature.shape)
reverse_feature = ReverseLayerF.apply(input_data, alpha)
# print(reverse_feature.shape)
class_output = self.class_classifier(input_data)
# print(class_output.shape)
domain_output = self.domain_classifier(reverse_feature)
# print(class_output.shape)
# print(domain_output)
return class_output, domain_output
def forward(self, input_data, alpha, return_ddc_features=None):
if return_ddc_features is not None:
assert return_ddc_features in self.class_classifier._modules
input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)
feature = self.feature(input_data)
feature = feature.view(-1, 50 * 4 * 4)
reverse_feature = ReverseLayerF.apply(feature, alpha)
# class_output = self.class_classifier(feature)
class_output = feature
ddc_features = None
for k, v in self.class_classifier._modules.items():
class_output = v(class_output)
if k == return_ddc_features:
ddc_features = class_output
domain_output = self.domain_classifier(reverse_feature)
if return_ddc_features:
return class_output, domain_output, ddc_features
else:
return class_output, domain_output
def forward(self, input_data, alpha=1.0, return_ddc_features=None):
if return_ddc_features is not None:
assert return_ddc_features in self.class_classifier._modules or return_ddc_features == "tabnet_features"
# feature = self.embedder(input_data)
feature, M_loss = self.tabnet(input_data)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = feature
ddc_features = feature
for k, v in self.class_classifier._modules.items():
class_output = v(class_output)
if k == return_ddc_features:
ddc_features = class_output
domain_output = self.domain_classifier(reverse_feature)
if return_ddc_features:
return class_output, domain_output, ddc_features, M_loss
else:
return class_output, domain_output, M_loss
def forward(self, input_data, mode, Rec_scheme, alpha):
if mode == 'source':
private_enc = self.private_enc_src
pri_enc_fc = self.private_fc_src
else:
input_data = input_data.expand(input_data.data.shape[0],
self.channels, 32, 32)
private_enc = self.private_enc_tgt
pri_enc_fc = self.private_fc_src
#private encoder
private_feat = private_enc(input_data)
private_feat = private_feat.view(-1, 64 * 8 * 8)
private_feat_code = pri_enc_fc(private_feat)
#shared encoder
shared_feat = self.shared_enc(input_data)
shared_feat = shared_feat.view(-1, 64 * 8 * 8)
shared_feat_code = self.shd_enc_fc(shared_feat)
#label classifier
pred_label = self.classifier(shared_feat_code)
#domain classifier
reverse_feature = ReverseLayerF.apply(shared_feat_code, alpha)
pred_domain = self.domain_dis(reverse_feature)
#shared decoder
if Rec_scheme == 'private':
rec_feat_code = private_feat_code
if Rec_scheme == 'shared':
rec_feat_code = shared_feat_code
if Rec_scheme == 'all':
rec_feat_code = private_feat_code + shared_feat_code
feat_encode = self.shd_dec_fc(rec_feat_code)
feat_encode = feat_encode.view(-1, 3, 10, 10)
img_rec = self.shared_dec(feat_encode)
return private_feat_code, shared_feat_code, pred_label, pred_domain, img_rec
def forward(self, input_data, alpha, training):
# input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)
input_data = input_data.expand(input_data.data.shape[0], 3, 32, 32)
# feature = self.feature(input_data)
feature = self.feature1(input_data)
if training:
feature = self.dropout1(feature)
feature += torch.zeros(feature.size()).normal_(0, 1).cuda()
feature = self.feature2(feature)
if training:
feature = self.dropout2(feature)
feature += torch.zeros(feature.size()).normal_(0, 1).cuda()
class_output = self.class_classifier(feature)
# feature = feature.view(-1, 50 * 4 * 4)
feature = feature.view(feature.shape[0], -1)
reverse_feature = ReverseLayerF.apply(feature, alpha)
# class_output = self.class_classifier(feature)
domain_output = self.domain_classifier(reverse_feature)
return class_output, domain_output
def forward(self, x, iter_num):
coefficient = self.calc_coeff(iter_num)
reversed_x = ReverseLayerF.apply(x, coefficient)
return self.forward_pass(reversed_x)
def forward(self,
im_data,
im_info,
gt_boxes,
num_boxes,
domain=None,
l=0,
loss_start=False):
batch_size = im_data.size(0)
im_info = im_info.data
gt_boxes = gt_boxes.data
num_boxes = num_boxes.data
# feed image data to base model to obtain base feature map
base_feat = self.RCNN_base(im_data)
# feed base feature map tp RPN to obtain rois
rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(
base_feat, im_info, gt_boxes, num_boxes)
# if it is training phrase, then use ground trubut bboxes for refining
if self.training:
roi_data = self.RCNN_proposal_target(rois, gt_boxes, num_boxes,
domain, self.transfer)
rois, rois_label, rois_target, rois_inside_ws, rois_outside_ws, domain_label = roi_data
rois_label = Variable(rois_label.view(-1).long())
rois_target = Variable(rois_target.view(-1, rois_target.size(2)))
rois_inside_ws = Variable(
rois_inside_ws.view(-1, rois_inside_ws.size(2)))
rois_outside_ws = Variable(
rois_outside_ws.view(-1, rois_outside_ws.size(2)))
else:
rois_label = None
rois_target = None
rois_inside_ws = None
rois_outside_ws = None
rpn_loss_cls = 0
rpn_loss_bbox = 0
rois = Variable(rois)
# do roi pooling based on predicted rois
if cfg.POOLING_MODE == 'crop':
# pdb.set_trace()
# pooled_feat_anchor = _crop_pool_layer(base_feat, rois.view(-1, 5))
grid_xy = _affine_grid_gen(rois.view(-1, 5),
base_feat.size()[2:], self.grid_size)
grid_yx = torch.stack(
[grid_xy.data[:, :, :, 1], grid_xy.data[:, :, :, 0]],
3).contiguous()
pooled_feat = self.RCNN_roi_crop(base_feat,
Variable(grid_yx).detach())
if cfg.CROP_RESIZE_WITH_MAX_POOL:
pooled_feat = F.max_pool2d(pooled_feat, 2, 2)
elif cfg.POOLING_MODE == 'align':
pooled_feat = self.RCNN_roi_align(base_feat, rois.view(-1, 5))
elif cfg.POOLING_MODE == 'pool':
pooled_feat = self.RCNN_roi_pool(base_feat, rois.view(-1, 5))
#-----------------------transfer learning----------------------------#
#print(domain)
dom_loss = 0
#base line: transfer == False
if self.training and self.transfer:
if self.grl:
dom_input = ReverseLayerF.apply(pooled_feat, l)
else:
dom_input = pooled_feat
dom_pred = self._domain_classify(dom_input)
domain_label = Variable(domain_label.cpu().cuda().view(-1).long())
############Process Tranfer Loss Weight#########
if loss_start:
p_target = F.softmax(dom_pred * self.transfer_gamma)[:, 0]
domain_label.data = domain_label.data.type(
torch.FloatTensor).cuda()
l_target = domain_label
self.weight = p_target**l_target
###############################################
##############DOMAIN LOSS SELECTION##########
else:
ids = torch.LongTensor(1).cuda()
# random select
if self.transfer_select == 'RANDOM':
perm = torch.randperm(rois.size(1))
ids = perm[:rois.size(1) / 8].cuda()
# select positive sample and predicted postive sample
elif self.transfer_select == 'CONDITION':
ids = torch.range(0, rois.size(1) / 8 - 1)
ids = torch.Tensor.long(ids).cuda()
# select all postive sample
elif self.transfer_select == 'POSITIVE':
ids = torch.nonzero(rois_label.data)
ids = torch.squeeze(ids).cuda()
# select all postive sample
elif self.transfer_select == 'BALANCE':
ids_p = torch.nonzero(rois_label.data)
ids_p = torch.squeeze(ids_p).cuda()
ids_n = (rois_label.data == 0).nonzero()
ids_n = torch.squeeze(ids_n).cuda()
ids_n = ids_n[:ids_p.size(0)]
ids = torch.cat((ids_p, ids_n), 0).cuda()
# select all sample
if self.transfer_select == 'ALL':
dom_pred_loss = dom_pred
dom_label_loss = domain_label
else:
dom_pred_loss = dom_pred[ids]
dom_label_loss = domain_label[ids]
##########DOMAIN LOSS SELECTION DONE##########
dom_loss = F.cross_entropy(dom_pred_loss, dom_label_loss)
dom_loss = dom_loss * (
self.transfer_weight.expand_as(dom_loss))
#---------------------transfer learning done-------------------------#
# feed pooled features to top model
pooled_feat = self._head_to_tail(pooled_feat)
# compute bbox offset
bbox_pred = self.RCNN_bbox_pred(pooled_feat)
if self.training and not self.class_agnostic:
# select the corresponding columns according to roi labels
bbox_pred_view = bbox_pred.view(bbox_pred.size(0),
int(bbox_pred.size(1) / 4), 4)
bbox_pred_select = torch.gather(
bbox_pred_view, 1,
rois_label.view(rois_label.size(0), 1,
1).expand(rois_label.size(0), 1, 4))
bbox_pred = bbox_pred_select.squeeze(1)
# compute object classification probability
cls_score = self.RCNN_cls_score(pooled_feat)
cls_prob = F.softmax(cls_score)
RCNN_loss_cls = 0
RCNN_loss_bbox = 0
if self.training:
# classification loss
if self.transfer and loss_start:
rois_label_loss = torch.eye(
self.n_classes)[rois_label.data.cpu()].type(
torch.FloatTensor)
rois_label_loss = Variable(rois_label_loss.cuda())
weight_loss_cls = self.weight.view(rois_label.size(0),
1).repeat(
1, self.n_classes)
RCNN_loss_cls = F.binary_cross_entropy_with_logits(
cls_score, rois_label_loss, weight_loss_cls)
# bounding box regression L1 loss
RCNN_loss_bbox = _smooth_l1_loss(bbox_pred, rois_target,
rois_inside_ws,
rois_outside_ws, True, True,
self.weight)
else:
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)
cls_prob = cls_prob.view(batch_size, rois.size(1), -1)
bbox_pred = bbox_pred.view(batch_size, rois.size(1), -1)
return rois, cls_prob, bbox_pred, rpn_loss_cls, rpn_loss_bbox, RCNN_loss_cls, RCNN_loss_bbox, rois_label, dom_loss
