Fix typo tranpose to transpose
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jscsmk
parent
9efed05d53
commit
002263e07f
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@ -4,7 +4,7 @@ from __future__ import print_function
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import torch
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import torch.nn as nn
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from .utils import _gather_feat, _tranpose_and_gather_feat
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from .utils import _gather_feat, _transpose_and_gather_feat
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def _nms(heat, kernel=3):
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pad = (kernel - 1) // 2
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@ -213,13 +213,13 @@ def agnex_ct_decode(
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if t_regr is not None and l_regr is not None \
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and b_regr is not None and r_regr is not None:
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t_regr = _tranpose_and_gather_feat(t_regr, t_inds)
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t_regr = _transpose_and_gather_feat(t_regr, t_inds)
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t_regr = t_regr.view(batch, K, 1, 1, 1, 2)
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l_regr = _tranpose_and_gather_feat(l_regr, l_inds)
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l_regr = _transpose_and_gather_feat(l_regr, l_inds)
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l_regr = l_regr.view(batch, 1, K, 1, 1, 2)
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b_regr = _tranpose_and_gather_feat(b_regr, b_inds)
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b_regr = _transpose_and_gather_feat(b_regr, b_inds)
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b_regr = b_regr.view(batch, 1, 1, K, 1, 2)
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r_regr = _tranpose_and_gather_feat(r_regr, r_inds)
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r_regr = _transpose_and_gather_feat(r_regr, r_inds)
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r_regr = r_regr.view(batch, 1, 1, 1, K, 2)
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t_xs = t_xs + t_regr[..., 0]
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@ -365,13 +365,13 @@ def exct_decode(
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if t_regr is not None and l_regr is not None \
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and b_regr is not None and r_regr is not None:
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t_regr = _tranpose_and_gather_feat(t_regr, t_inds)
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t_regr = _transpose_and_gather_feat(t_regr, t_inds)
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t_regr = t_regr.view(batch, K, 1, 1, 1, 2)
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l_regr = _tranpose_and_gather_feat(l_regr, l_inds)
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l_regr = _transpose_and_gather_feat(l_regr, l_inds)
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l_regr = l_regr.view(batch, 1, K, 1, 1, 2)
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b_regr = _tranpose_and_gather_feat(b_regr, b_inds)
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b_regr = _transpose_and_gather_feat(b_regr, b_inds)
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b_regr = b_regr.view(batch, 1, 1, K, 1, 2)
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r_regr = _tranpose_and_gather_feat(r_regr, r_inds)
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r_regr = _transpose_and_gather_feat(r_regr, r_inds)
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r_regr = r_regr.view(batch, 1, 1, 1, K, 2)
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t_xs = t_xs + t_regr[..., 0]
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@ -431,7 +431,7 @@ def ddd_decode(heat, rot, depth, dim, wh=None, reg=None, K=40):
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scores, inds, clses, ys, xs = _topk(heat, K=K)
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if reg is not None:
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reg = _tranpose_and_gather_feat(reg, inds)
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reg = _transpose_and_gather_feat(reg, inds)
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reg = reg.view(batch, K, 2)
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xs = xs.view(batch, K, 1) + reg[:, :, 0:1]
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ys = ys.view(batch, K, 1) + reg[:, :, 1:2]
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@ -439,11 +439,11 @@ def ddd_decode(heat, rot, depth, dim, wh=None, reg=None, K=40):
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xs = xs.view(batch, K, 1) + 0.5
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ys = ys.view(batch, K, 1) + 0.5
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rot = _tranpose_and_gather_feat(rot, inds)
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rot = _transpose_and_gather_feat(rot, inds)
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rot = rot.view(batch, K, 8)
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depth = _tranpose_and_gather_feat(depth, inds)
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depth = _transpose_and_gather_feat(depth, inds)
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depth = depth.view(batch, K, 1)
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dim = _tranpose_and_gather_feat(dim, inds)
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dim = _transpose_and_gather_feat(dim, inds)
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dim = dim.view(batch, K, 3)
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clses = clses.view(batch, K, 1).float()
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scores = scores.view(batch, K, 1)
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@ -451,7 +451,7 @@ def ddd_decode(heat, rot, depth, dim, wh=None, reg=None, K=40):
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ys = ys.view(batch, K, 1)
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if wh is not None:
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wh = _tranpose_and_gather_feat(wh, inds)
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wh = _transpose_and_gather_feat(wh, inds)
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wh = wh.view(batch, K, 2)
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detections = torch.cat(
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[xs, ys, scores, rot, depth, dim, wh, clses], dim=2)
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@ -470,14 +470,14 @@ def ctdet_decode(heat, wh, reg=None, cat_spec_wh=False, K=100):
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scores, inds, clses, ys, xs = _topk(heat, K=K)
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if reg is not None:
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reg = _tranpose_and_gather_feat(reg, inds)
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reg = _transpose_and_gather_feat(reg, inds)
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reg = reg.view(batch, K, 2)
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xs = xs.view(batch, K, 1) + reg[:, :, 0:1]
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ys = ys.view(batch, K, 1) + reg[:, :, 1:2]
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else:
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xs = xs.view(batch, K, 1) + 0.5
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ys = ys.view(batch, K, 1) + 0.5
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wh = _tranpose_and_gather_feat(wh, inds)
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wh = _transpose_and_gather_feat(wh, inds)
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if cat_spec_wh:
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wh = wh.view(batch, K, cat, 2)
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clses_ind = clses.view(batch, K, 1, 1).expand(batch, K, 1, 2).long()
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@ -503,19 +503,19 @@ def multi_pose_decode(
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heat = _nms(heat)
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scores, inds, clses, ys, xs = _topk(heat, K=K)
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kps = _tranpose_and_gather_feat(kps, inds)
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kps = _transpose_and_gather_feat(kps, inds)
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kps = kps.view(batch, K, num_joints * 2)
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kps[..., ::2] += xs.view(batch, K, 1).expand(batch, K, num_joints)
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kps[..., 1::2] += ys.view(batch, K, 1).expand(batch, K, num_joints)
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if reg is not None:
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reg = _tranpose_and_gather_feat(reg, inds)
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reg = _transpose_and_gather_feat(reg, inds)
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reg = reg.view(batch, K, 2)
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xs = xs.view(batch, K, 1) + reg[:, :, 0:1]
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ys = ys.view(batch, K, 1) + reg[:, :, 1:2]
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else:
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xs = xs.view(batch, K, 1) + 0.5
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ys = ys.view(batch, K, 1) + 0.5
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wh = _tranpose_and_gather_feat(wh, inds)
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wh = _transpose_and_gather_feat(wh, inds)
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wh = wh.view(batch, K, 2)
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clses = clses.view(batch, K, 1).float()
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scores = scores.view(batch, K, 1)
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@ -532,7 +532,7 @@ def multi_pose_decode(
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reg_kps = kps.unsqueeze(3).expand(batch, num_joints, K, K, 2)
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hm_score, hm_inds, hm_ys, hm_xs = _topk_channel(hm_hp, K=K) # b x J x K
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if hp_offset is not None:
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hp_offset = _tranpose_and_gather_feat(
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hp_offset = _transpose_and_gather_feat(
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hp_offset, hm_inds.view(batch, -1))
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hp_offset = hp_offset.view(batch, num_joints, K, 2)
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hm_xs = hm_xs + hp_offset[:, :, :, 0]
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@ -10,7 +10,7 @@ from __future__ import print_function
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import torch
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import torch.nn as nn
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from .utils import _tranpose_and_gather_feat
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from .utils import _transpose_and_gather_feat
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import torch.nn.functional as F
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@ -132,7 +132,7 @@ class RegLoss(nn.Module):
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super(RegLoss, self).__init__()
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def forward(self, output, mask, ind, target):
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pred = _tranpose_and_gather_feat(output, ind)
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pred = _transpose_and_gather_feat(output, ind)
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loss = _reg_loss(pred, target, mask)
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return loss
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@ -141,7 +141,7 @@ class RegL1Loss(nn.Module):
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super(RegL1Loss, self).__init__()
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def forward(self, output, mask, ind, target):
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pred = _tranpose_and_gather_feat(output, ind)
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pred = _transpose_and_gather_feat(output, ind)
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mask = mask.unsqueeze(2).expand_as(pred).float()
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# loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
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loss = F.l1_loss(pred * mask, target * mask, size_average=False)
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@ -153,7 +153,7 @@ class NormRegL1Loss(nn.Module):
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super(NormRegL1Loss, self).__init__()
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def forward(self, output, mask, ind, target):
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pred = _tranpose_and_gather_feat(output, ind)
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pred = _transpose_and_gather_feat(output, ind)
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mask = mask.unsqueeze(2).expand_as(pred).float()
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# loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
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pred = pred / (target + 1e-4)
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@ -167,7 +167,7 @@ class RegWeightedL1Loss(nn.Module):
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super(RegWeightedL1Loss, self).__init__()
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def forward(self, output, mask, ind, target):
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pred = _tranpose_and_gather_feat(output, ind)
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pred = _transpose_and_gather_feat(output, ind)
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mask = mask.float()
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# loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
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loss = F.l1_loss(pred * mask, target * mask, size_average=False)
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@ -179,7 +179,7 @@ class L1Loss(nn.Module):
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super(L1Loss, self).__init__()
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def forward(self, output, mask, ind, target):
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pred = _tranpose_and_gather_feat(output, ind)
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pred = _transpose_and_gather_feat(output, ind)
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mask = mask.unsqueeze(2).expand_as(pred).float()
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loss = F.l1_loss(pred * mask, target * mask, reduction='elementwise_mean')
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return loss
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@ -189,7 +189,7 @@ class BinRotLoss(nn.Module):
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super(BinRotLoss, self).__init__()
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def forward(self, output, mask, ind, rotbin, rotres):
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pred = _tranpose_and_gather_feat(output, ind)
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pred = _transpose_and_gather_feat(output, ind)
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loss = compute_rot_loss(pred, rotbin, rotres, mask)
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return loss
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@ -19,7 +19,7 @@ def _gather_feat(feat, ind, mask=None):
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feat = feat.view(-1, dim)
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return feat
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def _tranpose_and_gather_feat(feat, ind):
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def _transpose_and_gather_feat(feat, ind):
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feat = feat.permute(0, 2, 3, 1).contiguous()
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feat = feat.view(feat.size(0), -1, feat.size(3))
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feat = _gather_feat(feat, ind)
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