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config.py
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config.py
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import os
import pickle
import shutil
import glob
import datetime
class train_config:
def __init__(self,name,recipes=[],**params):
###basic configs
self.name=name
self.comment=''
self.workers = 10
self.epochs = 10
self.learning_rate = 1e-4
self.adam_betas=(0.9,0.999)
self.weight_decay=1e-6
self.scheduler_step=5
self.scheduler_gamma=0.5
self.ckpt = ''
self.resume_optim=False
self.freeze=[]
self.url='tcp://127.0.0.1:27015'
###net config
self.num_classes=2
self.num_attentions=4
self.attention_layer='b5'
self.feature_layer='b1'
self.mid_dims=256
self.dropout_rate=0.25
self.drop_final_rate=0.5
self.pretrained=''
self.alpha=0.05
self.alpha_decay=0.9
self.margin=0.5
self.inner_margin=[0.1,-2]
###AGDA configs
self.AGDA_kernel_size=11
self.AGDA_dilation=2
self.AGDA_sigma=7
self.AGDA_scale_factor=0.5
self.AGDA_threshold=(0.4,0.6)
self.AGDA_zoom=(3,5)
self.AGDA_noise_rate=0.1
self.AGDA_mode='soft'
###loss configs
self.ensemble_loss_weight=1
self.aux_loss_weight=0.5
self.AGDA_loss_weight=1
self.match_loss_weight=0.1
###cook
for i in recipes:
self.recipe(i)
for i in params:
self.__setattr__(i,params[i])
self.train_dataset=dict(datalabel=self.datalabel, resize=self.resize,imgs_per_video=self.imgs_per_video,normalize=self.normalize,\
frame_interval=self.frame_interval,max_frames=self.max_frames,augment=self.augment)
self.val_dataset=self.train_dataset
self.net_config=dict(net=self.net,feature_layer=self.feature_layer,attention_layer=self.attention_layer,num_classes=self.num_classes, M=self.num_attentions,\
mid_dims=self.mid_dims,dropout_rate=self.dropout_rate,drop_final_rate=self.drop_final_rate,pretrained=self.pretrained,alpha=self.alpha,size=self.resize,margin=self.margin,inner_margin=self.inner_margin)
self.AGDA_config=dict(kernel_size=self.AGDA_kernel_size,dilation=self.AGDA_dilation,sigma=self.AGDA_sigma,scale_factor=self.AGDA_scale_factor,threshold=self.AGDA_threshold,zoom=self.AGDA_zoom,noise_rate=self.AGDA_noise_rate,mode=self.AGDA_mode)
def recipe(self,name):
if 'ff-' in name:
if 'ff-5' in name:
self.num_classes=5
self.datalabel=name
self.imgs_per_video=50
self.frame_interval=10
self.max_frames=500
self.augment='augment0'
if 'dfdc' in name:
self.datalabel='dfdc'
self.max_frames=300
self.imgs_per_video=30
self.frame_interval=10
self.augment='augment2'
if 'xception' in name:
self.net='xception'
self.batch_size=32
self.resize=(299,299)
self.normalize=dict(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5])
if 'efficient' in name:
self.net=name
self.batch_size=10
self.normalize=dict(mean=[0.5,0.5,0.5],std=[0.5,0.5,0.5])
scale=int(name.split('b')[-1])
sizes=[224,240,260,300,380,456,528,600,672]
self.resize=(sizes[scale],sizes[scale])
def mkdirs(self):
os.makedirs('checkpoints/'+self.name,exist_ok=True)
os.makedirs('runs/'+self.name,exist_ok=True)
os.makedirs('evaluations/'+self.name,exist_ok=True)
with open('runs/%s/config.pkl'%self.name,'wb') as f:
pickle.dump(self,f)
if not self.comment:
self.comment=self.name+'_'+datetime.datetime.now().isoformat()
os.system('git add . && git commit -m "{}" && git tag {} -f'.format(self.comment,self.name))
@staticmethod
def load(name):
with open('runs/%s/config.pkl'%name,'rb') as f:
p=pickle.load(f)
v=train_config('',['ff-','xception'])
p=vars(p)
for i in p:
setattr(v,i,p[i])
return v
def reload(self,only_backnone=False):
list_of_files = glob.glob('checkpoints/%s/*'%self.name)
num=len(list_of_files)
latest_file = max(list_of_files, key=os.path.getctime)
if num>=0:
if not only_backnone:
self.ckpt=latest_file
else:
self.pretrained=latest_file