ZF/insightface
1
0
Fork 0
mirror of https://github.com/deepinsight/insightface.git synced 2026-05-17 22:27:54 +00:00
Code Issues Packages Projects Releases Wiki Activity

updated for WebFace42M

Browse Source 
updated readability of the code
This commit is contained in:
AnXiang
2022-01-14 17:43:40 +08:00
parent c2b52f44a6
commit bb221e6e6d
31 changed files with 798 additions and 752 deletions
Download Patch File Download Diff File Expand all files Collapse all files

37
recognition/arcface_torch/utils/utils_callbacks.py
View File

@@ -7,12 +7,14 @@ import torch
from eval import verification
from utils.utils_logging import AverageMeter
from torch.utils.tensorboard import SummaryWriter
from torch import distributed
class CallBackVerification(object):
def __init__(self, frequent, rank, val_targets, rec_prefix, image_size=(112, 112)):
self.frequent: int = frequent
self.rank: int = rank
def __init__(self, val_targets, rec_prefix, summary_writer=None, image_size=(112, 112)):
self.rank: int = distributed.get_rank()
self.highest_acc: float = 0.0
self.highest_acc_list: List[float] = [0.0] * len(val_targets)
self.ver_list: List[object] = []
@@ -20,6 +22,8 @@ class CallBackVerification(object):
if self.rank is 0:
self.init_dataset(val_targets=val_targets, data_dir=rec_prefix, image_size=image_size)
self.summary_writer = summary_writer
def ver_test(self, backbone: torch.nn.Module, global_step: int):
results = []
for i in range(len(self.ver_list)):
@@ -27,6 +31,10 @@ class CallBackVerification(object):
self.ver_list[i], backbone, 10, 10)
logging.info('[%s][%d]XNorm: %f' % (self.ver_name_list[i], global_step, xnorm))
logging.info('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (self.ver_name_list[i], global_step, acc2, std2))
self.summary_writer: SummaryWriter
self.summary_writer.add_scalar(tag=self.ver_name_list[i], scalar_value=acc2, global_step=global_step, )
if acc2 > self.highest_acc_list[i]:
self.highest_acc_list[i] = acc2
logging.info(
@@ -42,20 +50,20 @@ class CallBackVerification(object):
self.ver_name_list.append(name)
def __call__(self, num_update, backbone: torch.nn.Module):
if self.rank is 0 and num_update > 0 and num_update % self.frequent == 0:
if self.rank is 0 and num_update > 0:
backbone.eval()
self.ver_test(backbone, num_update)
backbone.train()
class CallBackLogging(object):
def __init__(self, frequent, rank, total_step, batch_size, world_size, writer=None):
def __init__(self, frequent, total_step, batch_size, writer=None):
self.frequent: int = frequent
self.rank: int = rank
self.rank: int = distributed.get_rank()
self.world_size: int = distributed.get_world_size()
self.time_start = time.time()
self.total_step: int = total_step
self.batch_size: int = batch_size
self.world_size: int = world_size
self.writer = writer
self.init = False
@@ -100,18 +108,3 @@ class CallBackLogging(object):
else:
self.init = True
self.tic = time.time()
class CallBackModelCheckpoint(object):
def __init__(self, rank, output="./"):
self.rank: int = rank
self.output: str = output
def __call__(self, global_step, backbone, partial_fc, ):
if global_step > 100 and self.rank == 0:
path_module = os.path.join(self.output, "backbone.pth")
torch.save(backbone.module.state_dict(), path_module)
logging.info("Pytorch Model Saved in '{}'".format(path_module))
if global_step > 100 and partial_fc is not None:
partial_fc.save_params()