def forward(self,fast_feature, proposal_bboxes: Tensor,
gt_classes_batch: Optional[Tensor] = None, gt_bboxes_batch: Optional[Tensor] = None) \
-> Union[Tuple[Tensor], Tuple[Tensor, Tensor, Tensor, Tensor]]: ## slow_feature, , Tensor
batch_size = fast_feature.shape[0] ## fast_feature:{1,256,16,16]
feature = nn.AvgPool3d(
kernel_size=(fast_feature.shape[2], 1,
1))(fast_feature).squeeze(
2) ## feature: [1,2304,16,16]
# slow_feature = nn.AvgPool3d(kernel_size=(slow_feature.shape[2], 1, 1))(slow_feature).squeeze(2)
# feature=torch.cat([fast_feature, slow_feature],dim=1)
if not self.training:
assert batch_size == 1
proposal_batch_indices = torch.arange(
end=batch_size,
dtype=torch.long,
device=proposal_bboxes.device).view(-1, 1).repeat(
1, proposal_bboxes.shape[0])[0]
pool = Pooler.apply(feature,
proposal_bboxes,
proposal_batch_indices,
mode=Pooler.Mode.POOLING)
hidden = self.hidden(pool)
proposal_classes = self._proposal_class(hidden)
return proposal_classes
else:
#过滤掉补充的0
# find labels for each `proposal_bboxes`
ious = BBox.iou(proposal_bboxes, gt_bboxes_batch)
proposal_max_ious, proposal_assignments = ious.max(dim=2)
fg_masks = proposal_max_ious >= 0.85
if len(fg_masks.nonzero()) > 0:
#fg_masks.nonzero()[:, 0]是在获取batch
proposal_bboxes = proposal_bboxes[fg_masks.nonzero()[:, 0],
fg_masks.nonzero()[:, 1]]
batch_indices = fg_masks.nonzero()[:, 0]
labels = gt_classes_batch[fg_masks.nonzero()[:, 0],
proposal_assignments[fg_masks]]
else:
print('bbox warning')
fg_masks = proposal_max_ious >= 0.5
proposal_bboxes = proposal_bboxes[fg_masks.nonzero()[:, 0],
fg_masks.nonzero()[:, 1]]
batch_indices = fg_masks.nonzero()[:, 0]
labels = gt_classes_batch[fg_masks.nonzero()[:, 0],
proposal_assignments[fg_masks]]
# # #空间池化，拼接
pool = Pooler.apply(feature,
proposal_bboxes,
batch_indices,
mode=Pooler.Mode.POOLING)
# print("******** pool shape *******", pool.shape) [6, 2048, 3, 3]
hidden = self.hidden(pool)
proposal_classes = self._proposal_class(hidden)
proposal_class_losses = self.loss(proposal_classes, labels,
batch_size, batch_indices)
return proposal_classes, proposal_class_losses
def forward(self, features: Tensor, proposal_bboxes: Tensor,
gt_classes_batch: Optional[Tensor] = None, gt_bboxes_batch: Optional[Tensor] = None) -> Union[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor, Tensor, Tensor]]:
batch_size = features.shape[0]
if not self.training:
proposal_batch_indices = torch.arange(end=batch_size, dtype=torch.long, device=proposal_bboxes.device).view(-1, 1).repeat(1, proposal_bboxes.shape[1])
pool = Pooler.apply(features, proposal_bboxes.view(-1, 4), proposal_batch_indices.view(-1), mode=self._pooler_mode)
hidden = self.hidden(pool)
hidden = F.adaptive_max_pool2d(input=hidden, output_size=1)
hidden = hidden.view(hidden.shape[0], -1)
proposal_classes = self._proposal_class(hidden)
proposal_transformers = self._proposal_transformer(hidden)
proposal_classes = proposal_classes.view(batch_size, -1, proposal_classes.shape[-1])
proposal_transformers = proposal_transformers.view(batch_size, -1, proposal_transformers.shape[-1])
return proposal_classes, proposal_transformers
else:
# find labels for each `proposal_bboxes`
labels = torch.full((batch_size, proposal_bboxes.shape[1]), -1, dtype=torch.long, device=proposal_bboxes.device)
ious = BBox.iou(proposal_bboxes, gt_bboxes_batch)
proposal_max_ious, proposal_assignments = ious.max(dim=2)
labels[proposal_max_ious < 0.5] = 0
fg_masks = proposal_max_ious >= 0.5
if len(fg_masks.nonzero()) > 0:
labels[fg_masks] = gt_classes_batch[fg_masks.nonzero()[:, 0], proposal_assignments[fg_masks]]
# select 128 x `batch_size` samples
fg_indices = (labels > 0).nonzero()
bg_indices = (labels == 0).nonzero()
fg_indices = fg_indices[torch.randperm(len(fg_indices))[:min(len(fg_indices), 32 * batch_size)]]
bg_indices = bg_indices[torch.randperm(len(bg_indices))[:128 * batch_size - len(fg_indices)]]
selected_indices = torch.cat([fg_indices, bg_indices], dim=0)
selected_indices = selected_indices[torch.randperm(len(selected_indices))].unbind(dim=1)
proposal_bboxes = proposal_bboxes[selected_indices]
gt_bboxes = gt_bboxes_batch[selected_indices[0], proposal_assignments[selected_indices]]
gt_proposal_classes = labels[selected_indices]
gt_proposal_transformers = BBox.calc_transformer(proposal_bboxes, gt_bboxes)
batch_indices = selected_indices[0]
pool = Pooler.apply(features, proposal_bboxes, proposal_batch_indices=batch_indices, mode=self._pooler_mode)
hidden = self.hidden(pool)
hidden = F.adaptive_max_pool2d(input=hidden, output_size=1)
hidden = hidden.view(hidden.shape[0], -1)
proposal_classes = self._proposal_class(hidden)
proposal_transformers = self._proposal_transformer(hidden)
proposal_class_losses, proposal_transformer_losses = self.loss(proposal_classes, proposal_transformers,
gt_proposal_classes, gt_proposal_transformers,
batch_size, batch_indices)
return proposal_classes, proposal_transformers, proposal_class_losses, proposal_transformer_losses
def forward(self, p2: Tensor, p3: Tensor, p4: Tensor, p5: Tensor,
proposal_bboxes: Tensor, image_width: int,
image_height: int) -> Tuple[Tensor, Tensor]:
w = proposal_bboxes[:, 2] - proposal_bboxes[:, 0]
h = proposal_bboxes[:, 3] - proposal_bboxes[:, 1]
k0 = 4
k = torch.floor(k0 + torch.log2(torch.sqrt(w * h) / 224)).long()
k = torch.clamp(k, min=2, max=5)
k_to_p_dict = {2: p2, 3: p3, 4: p4, 5: p5}
unique_k = torch.unique(k)
# NOTE: `picked_indices` is for recording the order of selection from `proposal_bboxes`
# so that `pools` can be then restored to make it have a consistent correspondence
# with `proposal_bboxes`. For example:
#
# proposal_bboxes => B0 B1 B2
# picked_indices => 1 2 0
# pools => BP1 BP2 BP0
# sorted_indices => 2 0 1
# pools => BP0 BP1 BP2
pools = []
picked_indices = []
for uk in unique_k:
uk = uk.item()
p = k_to_p_dict[uk]
uk_indices = (k == uk).nonzero().view(-1)
uk_proposal_bboxes = proposal_bboxes[uk_indices]
pool = Pooler.apply(p,
uk_proposal_bboxes,
mode=self._pooler_mode)
pools.append(pool)
picked_indices.append(uk_indices)
pools = torch.cat(pools, dim=0)
picked_indices = torch.cat(picked_indices, dim=0)
_, sorted_indices = torch.sort(picked_indices)
pools = pools[sorted_indices]
pools = pools.view(pools.shape[0], -1)
hidden = self._hidden(pools)
classes = self._class(hidden)
transformers = self._transformer(hidden)
return classes, transformers
def forward(
self,
fast_feature,
slow_feature,
proposal_bboxes: Tensor,
gt_classes_batch: Optional[Tensor] = None,
gt_bboxes_batch: Optional[Tensor] = None
) -> Union[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor, Tensor,
Tensor]]:
batch_size = fast_feature.shape[0]
fast_feature = nn.AvgPool3d(
kernel_size=(fast_feature.shape[2], 1,
1))(fast_feature).squeeze(2)
#print("fast_feature:",fast_feature.shape)
slow_feature = nn.AvgPool3d(
kernel_size=(slow_feature.shape[2], 1,
1))(slow_feature).squeeze(2)
feature = torch.cat([fast_feature, slow_feature], dim=1)
if not self.training:
assert batch_size == 1
# a=torch.arange(end=batch_size, dtype=torch.long, device=proposal_bboxes.device).view(-1, 1)
# b=a.repeat(1, proposal_bboxes.shape[1])
######################################
######## @ FATAL ERROR @ ###############
######################################
proposal_batch_indices = torch.arange(
end=batch_size,
dtype=torch.long,
device=proposal_bboxes.device).view(-1, 1).repeat(
1, proposal_bboxes.shape[0])[0]
# pool_f = Pooler.apply(fast_feature, proposal_bboxes.view(-1, 4), proposal_batch_indices.view(-1), mode=self._pooler_mode)
# pool_s = Pooler.apply(slow_feature, proposal_bboxes.view(-1, 4), proposal_batch_indices.view(-1), mode=self._pooler_mode)
# # 空间池化，拼接
# pool_f = nn.AdaptiveAvgPool3d((1, pool_f.shape[3], pool_f.shape[4]))(pool_f)
# pool_s = nn.AdaptiveAvgPool3d((1, pool_s.shape[3], pool_s.shape[4]))(pool_s)
# pool_f = pool_f.squeeze(2)
# pool_s = pool_s.squeeze(2)
# pool = torch.cat([pool_f, pool_s], dim=1)
pool = Pooler.apply(feature,
proposal_bboxes,
proposal_batch_indices,
mode=Pooler.Mode.POOLING)
hidden = self.hidden(pool)
proposal_classes = self._proposal_class(hidden)
#proposal_classes = proposal_classes.view(batch_size, -1, proposal_classes.shape[-1])
return proposal_classes
else:
#过滤掉补充的0
# find labels for each `proposal_bboxes`
ious = BBox.iou(proposal_bboxes, gt_bboxes_batch)
proposal_max_ious, proposal_assignments = ious.max(dim=2)
#_, proposal_which = ious.max(dim=1)
fg_masks = proposal_max_ious >= 0.85
if len(fg_masks.nonzero()) > 0:
#fg_masks.nonzero()[:, 0]是在获取batch
proposal_bboxes = proposal_bboxes[fg_masks.nonzero()[:, 0],
fg_masks.nonzero()[:, 1]]
batch_indices = fg_masks.nonzero()[:, 0]
labels = gt_classes_batch[fg_masks.nonzero()[:, 0],
proposal_assignments[fg_masks]]
else:
print('bbox warning')
fg_masks = proposal_max_ious >= 0.5
proposal_bboxes = proposal_bboxes[fg_masks.nonzero()[:, 0],
fg_masks.nonzero()[:, 1]]
batch_indices = fg_masks.nonzero()[:, 0]
labels = gt_classes_batch[fg_masks.nonzero()[:, 0],
proposal_assignments[fg_masks]]
# pool_f_pre= Pooler.apply(fast_feature, proposal_bboxes, batch_indices,mode=self._pooler_mode)
# pool_s_pre= Pooler.apply(slow_feature, proposal_bboxes,batch_indices, mode=self._pooler_mode)
# #空间池化，拼接
# pool_f_a = nn.AdaptiveAvgPool3d((1, pool_f_pre.shape[3], pool_f_pre.shape[4]))(pool_f_pre)
# pool_s_a = nn.AdaptiveAvgPool3d((1, pool_s_pre.shape[3], pool_s_pre.shape[4]))(pool_s_pre)
# pool_f_s = pool_f_a.squeeze(2)
# pool_s_s = pool_s_a.squeeze(2)
# pool=torch.cat([pool_f_s, pool_s_s], dim=1)
pool = Pooler.apply(feature,
proposal_bboxes,
batch_indices,
mode=Pooler.Mode.POOLING)
#sure 5
#print("cls_labels:",labels)
hidden = self.hidden(pool)
proposal_classes = self._proposal_class(hidden)
proposal_class_losses = self.loss(proposal_classes, labels,
batch_size, batch_indices)
return proposal_classes, proposal_class_losses
def forward(
self,
features: Tensor,
proposal_bboxes: Tensor,
gt_classes_batch: Optional[Tensor] = None,
gt_bboxes_batch: Optional[Tensor] = None
) -> Union[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor, Tensor,
Tensor]]:
#features@(bn,1024,h/16,w/16)
#pb@(bn,gp_n,4)
batch_size = features.shape[0]
if not self.training:
#(bn,gp_n)
proposal_batch_indices = torch.arange(
end=batch_size,
dtype=torch.long,
device=proposal_bboxes.device).view(-1, 1).repeat(
1, proposal_bboxes.shape[1])
#ROI pooling:(bn,gp_n,1024,7,7). 这里的ROI pooling相当于把features与pb进行合并
pool = Pooler.apply(features,
proposal_bboxes.view(-1, 4),
proposal_batch_indices.view(-1),
mode=self._pooler_mode)
hidden = self.hidden(
pool
) #(bn,gp_n,2048,4,4). 这里(7,7)变为(4,4)的原因是hidden中还有个stride=2的卷积层.
hidden = F.adaptive_max_pool2d(
input=hidden, output_size=1) #(bn,gp_n,2048,1,1)
hidden = hidden.view(hidden.shape[0], -1) #(bn,gp_n,2048)
#self._proposal_class@Linear(2048,num_cls) self._proposal_transformer@Linear(2048,4*num_cls)
proposal_classes = self._proposal_class(
hidden) #作分类的线性变换：(bn,gp_n,num_cls)
proposal_transformers = self._proposal_transformer(
hidden) #框回归：(bn,gp_n,num_cls*4)
proposal_classes = proposal_classes.view(
batch_size, -1,
proposal_classes.shape[-1]) #(bn,gp_n,num_cls)
proposal_transformers = proposal_transformers.view(
batch_size, -1,
proposal_transformers.shape[-1]) #(bn,gp_n,num_cls*4)
return proposal_classes, proposal_transformers
else:
#NOTE 总的处理流程类似rpn的训练forward: ious->labels->fg/bg->selected_indices.
# find labels for each `proposal_bboxes`
# 1.为每个pb找到类label@(bn,gp_n)，默认值为-1
labels = torch.full((batch_size, proposal_bboxes.shape[1]),
-1,
dtype=torch.long,
device=proposal_bboxes.device)
# ious@(bn,gp_n,gb_n) ious是确定labels的关键
ious = BBox.iou(
proposal_bboxes, gt_bboxes_batch
) #NOTE iou在detection.forward中只用到一次，在rpn.forward中也只用到一次
proposal_max_ious, proposal_assignments = ious.max(
dim=2) #(bn,gp_n)
labels[proposal_max_ious < 0.5] = 0 #背景类
fg_masks = proposal_max_ious >= 0.5 #(bn,gp_n) 前景类的条件
if len(fg_masks.nonzero()) > 0:
labels[fg_masks] = gt_classes_batch[
fg_masks.nonzero()[:,
0], proposal_assignments[fg_masks]]
# gt_classes_batch@(b,gt_n). 为前景类设置 类label
#现在labels共分为3种：
#a. 为0 ，对应背景proposal
#b. 为cls_label(>0),对应前景proposal
#c. 为-1的默认值，忽略
# select 128 x `batch_size` samples
#2.确定selected_indices, 选出 batch_size x 128 个训练样本
fg_indices = (labels > 0).nonzero() #(fg_n,2)
bg_indices = (labels == 0).nonzero() #(bg_n,2)
fg_indices = fg_indices[torch.randperm(
len(fg_indices))[:min(len(fg_indices), 32 *
batch_size)]] #最少len_fg，最多32个
bg_indices = bg_indices[torch.randperm(
len(bg_indices))[:128 * batch_size - len(fg_indices)]]
selected_indices = torch.cat([fg_indices, bg_indices],
dim=0) #(bn*128,2)
#分裂为元素为2的tuple，每个元素是上面的一列：([批内索引]，[pb内索引])
selected_indices = selected_indices[torch.randperm(
len(selected_indices))].unbind(dim=1)
proposal_bboxes = proposal_bboxes[
selected_indices] #(bn*128,4)
#以下是对gt_bboxes_batch的多维索引：selected_indices[0]是128个批内图像的索引，表示选择128个图像，
#proposal_assignments[selected_indices]表示128个图像对应的gt_box序号，也是128个
#最终选择了128个gt_bboxes.
gt_bboxes = gt_bboxes_batch[
selected_indices[0],
proposal_assignments[selected_indices]] #(bn*128,4)
gt_proposal_classes = labels[selected_indices] #(bn*128,)
gt_proposal_transformers = BBox.calc_transformer(
proposal_bboxes, gt_bboxes) #(bn*128,4)
batch_indices = selected_indices[0] #(bn*128)
#features@(bn,1024,w/16,h/16) proposal_bboxes@(bn*128,4)
#pool@(bn*128,1024,7,7)
pool = Pooler.apply(features,
proposal_bboxes,
proposal_batch_indices=batch_indices,
mode=self._pooler_mode)
hidden = self.hidden(pool) #(bn*128,2048,4,4)
hidden = F.adaptive_max_pool2d(
input=hidden, output_size=1) #(bn*128,2048,1,1)
hidden = hidden.view(hidden.shape[0], -1) #(bn*128,2048)
#self._proposal_class@Linear(2048,num_cls) self._proposal_transformer@Linear(2048,4*num_cls)
proposal_classes = self._proposal_class(
hidden) #(bn*128,num_cls)
proposal_transformers = self._proposal_transformer(
hidden) #(bn*128,num_cls*4)
proposal_class_losses, proposal_transformer_losses = self.loss(
proposal_classes, proposal_transformers,
gt_proposal_classes, gt_proposal_transformers, batch_size,
batch_indices)
return proposal_classes, proposal_transformers, proposal_class_losses, proposal_transformer_losses
def forward(
self,
features: Tensor,
proposal_bboxes: Tensor,
gt_classes_batch: Optional[Tensor] = None,
gt_bboxes_batch: Optional[Tensor] = None,
gt_vertices_batch: Optional[Tensor] = None,
image_width=None,
image_height=None
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[Tensor, Tensor, Tensor,
Tensor, Tensor]]:
batch_size = features.shape[0]
if not self.training:
proposal_batch_indices = torch.arange(
end=batch_size,
dtype=torch.long,
device=proposal_bboxes.device).view(-1, 1).repeat(
1, proposal_bboxes.shape[1])
pool = Pooler.apply(features,
proposal_bboxes.view(-1, 4),
proposal_batch_indices.view(-1),
mode=self._pooler_mode)
pool = pool.view(pool.shape[0], -1)
hidden = self.hidden[0](pool)
hidden = self.hidden[1](hidden)
hidden = self.hidden[3](hidden)
hidden = self.hidden[4](hidden)
#hidden = F.adaptive_max_pool2d(input=hidden, output_size=1)
#hidden = hidden.view(hidden.shape[0], -1)
proposal_transformers = self._proposal_transformer(hidden)
if self.iteration:
detection_bboxes = self.create_bboxes(
proposal_bboxes, proposal_transformers.unsqueeze(0),
image_width, image_height, 1)
detection_bboxes = detection_bboxes.view(-1, 4)
pool = Pooler.apply(features,
detection_bboxes.view(-1, 4),
proposal_batch_indices.view(-1),
mode=self._pooler_mode)
pool = pool.view(pool.shape[0], -1)
hidden = self.hidden[0](pool)
hidden = self.hidden[1](hidden)
hidden = self.hidden[3](hidden)
hidden = self.hidden[4](hidden)
proposal_transformers2 = self._proposal_transformer(hidden)
proposal_transformers2 = proposal_transformers2.view(
batch_size, -1, proposal_transformers.shape[-1])
else:
proposal_transformers2 = None
proposal_classes = self._proposal_class(hidden)
proposal_vertices = self._proposal_vertices(hidden)
proposal_classes = proposal_classes.view(
batch_size, -1, proposal_classes.shape[-1])
proposal_transformers = proposal_transformers.view(
batch_size, -1, proposal_transformers.shape[-1])
proposal_vertices = proposal_vertices.view(
batch_size, -1, proposal_vertices.shape[-1])
return proposal_vertices, proposal_classes, proposal_transformers, proposal_transformers2
else:
labels = torch.full((batch_size, proposal_bboxes.shape[1]),
-1,
dtype=torch.long,
device=proposal_bboxes.device)
# print(proposal_bboxes.size(), gt_bboxes_batch.size())
ious = BBox.iou(proposal_bboxes, gt_bboxes_batch)
#print(proposal_bboxes.size(), gt_bboxes_batch.size(), ious.size())
proposal_max_ious, proposal_assignments = ious.max(dim=2)
labels[proposal_max_ious < 0.5] = 0
fg_masks = proposal_max_ious >= 0.5
if len(fg_masks.nonzero()) > 0:
labels[fg_masks] = gt_classes_batch[
fg_masks.nonzero()[:,
0], proposal_assignments[fg_masks]]
# select 128 x `batch_size` samples
fg_indices = (labels > 0).nonzero()
bg_indices = (labels == 0).nonzero()
fg_indices = fg_indices[torch.randperm(
len(fg_indices))[:min(len(fg_indices), 32 * batch_size)]]
bg_indices = bg_indices[torch.randperm(
len(bg_indices))[:128 * batch_size - len(fg_indices)]]
selected_indices = torch.cat([fg_indices, bg_indices], dim=0)
selected_indices = selected_indices[torch.randperm(
len(selected_indices))].unbind(dim=1)
proposal_bboxes = proposal_bboxes[selected_indices]
gt_bboxes = gt_bboxes_batch[
selected_indices[0],
proposal_assignments[selected_indices]]
gt_vertices = gt_vertices_batch[
selected_indices[0],
proposal_assignments[selected_indices]]
gt_proposal_classes = labels[selected_indices]
gt_proposal_transformers = BBox.calc_transformer(
proposal_bboxes, gt_bboxes)
batch_indices = selected_indices[0]
#print('before', gt_proposal_classes)
#print(gt_proposal_classes.size())
pool = Pooler.apply(features,
proposal_bboxes,
proposal_batch_indices=batch_indices,
mode=self._pooler_mode)
#vgg16
hidden = self.hidden(pool.view(pool.shape[0], -1))
#resnet101
# hidden = self.hidden(pool)
# hidden = F.adaptive_max_pool2d(input=hidden, output_size=1)
# hidden = hidden.view(hidden.shape[0], -1)
if self.iteration:
proposal_transformers_first_iter = self._proposal_transformer(
hidden)
detection_bboxes = self.create_bboxes(
proposal_bboxes.unsqueeze(0),
proposal_transformers_first_iter.unsqueeze(0),
image_width, image_height, 1)
detection_bboxes = detection_bboxes.view(-1, 4)
pool = Pooler.apply(features,
detection_bboxes,
proposal_batch_indices=batch_indices,
mode=self._pooler_mode)
hidden = self.hidden(pool.view(pool.shape[0], -1))
bboxes_centers = torch.stack(
((detection_bboxes[:, 0] + detection_bboxes[:, 2]) / 2,
(detection_bboxes[:, 1] + detection_bboxes[:, 3]) /
2),
dim=1)
width = detection_bboxes[:, 2] - detection_bboxes[:, 0]
height = detection_bboxes[:, 3] - detection_bboxes[:, 1]
gt_proposal_transformers = BBox.calc_transformer(
detection_bboxes, gt_bboxes).detach()
for batch_index in range(batch_size):
selected_batch_indices = (
batch_indices == batch_index).nonzero().view(-1)
ious = BBox.iou(
detection_bboxes[selected_batch_indices].unsqueeze(
0), gt_bboxes_batch[batch_index].unsqueeze(
0)).detach()
#print('iter', detection_bboxes.size(), gt_bboxes.size(), ious.size())
max_ious, _ = ious.max(dim=2)
# print(gt_proposal_classes.size(), max_ious.size())
gt_proposal_classes[selected_batch_indices][
max_ious[0] < 0.5] = 0
gt_proposal_classes[selected_batch_indices][
max_ious[0] >= 0.5] = 1
#print('after', gt_proposal_classes)
#print(gt_proposal_classes.size())
# #if fg_indices.nelement() > 0:
# infinites = torch.isinf(gt_proposal_transformers)
# if gt_bboxes[gt_bboxes > 0].nelement() > 0 and infinites[infinites == 1].nelement() > 0:
# #print(infinites)
# #print(gt_proposal_transformers)
# # print(infinites.size())
# indices = torch.max(infinites,1)[0]
# #print(indices)
# indices = indices.nonzero().view(-1)
#print(indices)
#print('gt_proposal_transformers', gt_proposal_transformers[indices])
#print('detection_bboxes', detection_bboxes[indices])
#print('gt_bboxes', gt_bboxes[indices])
#print('ious', ious[0,index], ious.size())
#print(ious.size())
#print('max_ious', max_ious[0,indices], max_ious.size())
#print('gt_proposal_classes', gt_proposal_classes[indices], gt_proposal_classes.size())
# #yo = BBox.calc_transformer(detection_bboxes[index].unsqueeze(0), gt_bboxes[index].unsqueeze(0), print_it=True).detach()
else:
bboxes_centers = torch.stack(
((proposal_bboxes[:, 0] + proposal_bboxes[:, 2]) / 2,
(proposal_bboxes[:, 1] + proposal_bboxes[:, 3]) / 2),
dim=1)
width = proposal_bboxes[:, 2] - proposal_bboxes[:, 0]
height = proposal_bboxes[:, 3] - proposal_bboxes[:, 1]
gt_vertices_norm = torch.empty(gt_vertices.size(),
dtype=torch.float,
device=gt_vertices.device)
for i in range(gt_vertices_norm.size()[-1]):
gt_vertices_norm[:, :, i] = torch.stack(
((gt_vertices[:, 0, i] - bboxes_centers[:, 0]) / width,
(gt_vertices[:, 1, i] - bboxes_centers[:, 1]) /
height),
dim=1)
gt_vertices_norm = gt_vertices_norm.detach()
proposal_classes = self._proposal_class(hidden)
proposal_vertices = self._proposal_vertices(hidden)
proposal_transformers = self._proposal_transformer(hidden)
proposal_class_losses, proposal_transformer_losses, vertex_losses = self.loss(
proposal_vertices, proposal_classes, proposal_transformers,
gt_proposal_classes, gt_proposal_transformers,
gt_vertices_norm, batch_size, batch_indices)
return proposal_vertices, proposal_classes, proposal_transformers, proposal_class_losses, proposal_transformer_losses, vertex_losses