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1.0.0 official release (#18)

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* fix bug in fix_BN

* gaitgl OUMVLP support.

* update ./doc/3.advance_usage.md Cross-Dataset Evalution & Data Agumentation

* update config

* update docs.3

* update docs.3

* add loss doc and gather input decorator

* refine the create model doc

* support rearrange directory of unzipped OUMVLP

* fix some bugs in loss_aggregator.py

* refine docs and little fix

* add oumvlp pretreatment description

* pretreatment dataset fix oumvlp description

* add gaitgl oumvlp result

* assert gaitgl input size

* add pipeline

* update the readme.

* update pipeline and readme

* Corrigendum.

* add logo and remove path

* update new logo

* Update README.md

* modify logo size

Co-authored-by: 12131100 <12131100@mail.sustech.edu.cn>
Co-authored-by: noahshen98 <77523610+noahshen98@users.noreply.github.com>
Co-authored-by: Noah <595311942@qq.com>
This commit is contained in:
Junhao Liang
2021-12-08 20:05:28 +08:00
committed by GitHub
parent 6e71c7ac34
commit bb6cd5149a
39 changed files with 11401 additions and 230 deletions
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lib/modeling/losses/triplet.py
+7 -10
View File
@@ -1,22 +1,19 @@
import torch
import torch.nn.functional as F
from .base import BasicLoss
from utils import ddp_all_gather
from .base import BaseLoss, gather_and_scale_wrapper
class TripletLoss(BasicLoss):
class TripletLoss(BaseLoss):
def __init__(self, margin, loss_term_weights=1.0):
super(TripletLoss, self).__init__()
self.margin = margin
self.loss_term_weights = loss_term_weights
self.pair_based_loss = True
@gather_and_scale_wrapper
def forward(self, embeddings, labels):
# embeddings: [n, p, c], label: [n]
embeddings = ddp_all_gather(embeddings)
labels = ddp_all_gather(labels)
embeddings = embeddings.permute(
1, 0, 2).contiguous() # [n, p, c] -> [p, n, c]
embeddings = embeddings.float()
@@ -32,10 +29,10 @@ class TripletLoss(BasicLoss):
loss_avg, loss_num = self.AvgNonZeroReducer(loss)
self.info.update({
'loss': loss_avg,
'hard_loss': hard_loss,
'loss_num': loss_num,
'mean_dist': mean_dist})
'loss': loss_avg.detach().clone(),
'hard_loss': hard_loss.detach().clone(),
'loss_num': loss_num.detach().clone(),
'mean_dist': mean_dist.detach().clone()})
return loss_avg, self.info