crosstyan/OpenGait
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Solve the problem of dimension misuse. (#59)

Browse Source 
* commit for fix dimension

* fix dimension for all method

* restore config

* clean up baseline config

* add contiguous

* rm comment
This commit is contained in:
Junhao Liang
2022-06-28 12:27:16 +08:00
committed by GitHub
parent 715e7448fa
commit 14fa5212d4
14 changed files with 99 additions and 121 deletions
Download Patch File Download Diff File Expand all files Collapse all files
opengait/modeling/models/gln.py
+7 -9
View File
@@ -89,12 +89,12 @@ class GLN(BaseModel):
sils = ipts[0] # [n, s, h, w]
del ipts
if len(sils.size()) == 4:
sils = sils.unsqueeze(2)
n, s, _, h, w = sils.size()
sils = sils.unsqueeze(1)
n, _, s, h, w = sils.size()
### stage 0 sil ###
sil_0_outs = self.sil_stage_0(sils)
stage_0_sil_set = self.set_pooling(sil_0_outs, seqL, dim=1)[0]
stage_0_sil_set = self.set_pooling(sil_0_outs, seqL, options={"dim": 2})[0]
### stage 1 sil ###
sil_1_ipts = self.MaxP_sil(sil_0_outs)
@@ -105,13 +105,13 @@ class GLN(BaseModel):
sil_2_outs = self.sil_stage_2(sil_2_ipts)
### stage 1 set ###
set_1_ipts = self.set_pooling(sil_1_ipts, seqL, dim=1)[0]
stage_1_sil_set = self.set_pooling(sil_1_outs, seqL, dim=1)[0]
set_1_ipts = self.set_pooling(sil_1_ipts, seqL, options={"dim": 2})[0]
stage_1_sil_set = self.set_pooling(sil_1_outs, seqL, options={"dim": 2})[0]
set_1_outs = self.set_stage_1(set_1_ipts) + stage_1_sil_set
### stage 2 set ###
set_2_ipts = self.MaxP_set(set_1_outs)
stage_2_sil_set = self.set_pooling(sil_2_outs, seqL, dim=1)[0]
stage_2_sil_set = self.set_pooling(sil_2_outs, seqL, options={"dim": 2})[0]
set_2_outs = self.set_stage_2(set_2_ipts) + stage_2_sil_set
set1 = torch.cat((stage_0_sil_set, stage_0_sil_set), dim=1)
@@ -133,11 +133,9 @@ class GLN(BaseModel):
set2 = self.HPP(set2)
set3 = self.HPP(set3)
feature = torch.cat([set1, set2, set3], -
1).permute(2, 0, 1).contiguous()
feature = torch.cat([set1, set2, set3], -1)
feature = self.Head(feature)
feature = feature.permute(1, 0, 2).contiguous() # n p c
# compact_bloack
if not self.pretrain: