/
train_ATA.py
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train_ATA.py
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import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from methods.backbone import model_dict
from data.datamgr import SetDataManager
from methods.matchingnet import MatchingNet
from methods.relationnet import RelationNet
from methods.protonet import ProtoNet
from methods.gnnnet import GnnNet
from methods.tpn import TPN
from options import parse_args
def RCNN(X_n, params): # (5, 21, 3, 224, 224)
N, S, C, H, W = X_n.size()
p = np.random.rand()
K = [1, 3, 5, 7, 11, 15]
if p > params.prob:
k = K[np.random.randint(0, len(K))]
Conv = nn.Conv2d(3, 3, kernel_size=k, stride=1, padding=k//2, bias=False)
nn.init.xavier_normal_(Conv.weight)
X_n = Conv(X_n.reshape(-1, C, H, W)).reshape(N, S, C, H, W)
return X_n.detach()
def Max_phase(model, X_n):
X_n = X_n.cuda()
optimizer = optim.SGD([X_n.requires_grad_()], lr=params.max_lr)
model.eval()
for _ in range(params.T_max):
optimizer.zero_grad()
_, class_loss = model.set_forward_loss(X_n)
(-class_loss).backward()
optimizer.step()
return X_n.detach()
def train(base_loader, val_loader, model, start_epoch, stop_epoch, params):
max_acc = 0.
optimizer = torch.optim.Adam(model.parameters())
print_freq = len(base_loader)//10
for epoch in range(start_epoch, stop_epoch):
avg_loss = 0.
for i, (x, _) in enumerate(base_loader): # (5, 21, 3, 224, 224)
x = RCNN(x, params)
x_hat = Max_phase(model, x) # (5, 21, 3, 224, 224)
model.train()
optimizer.zero_grad()
_, loss = model.set_forward_loss(x_hat)
loss.backward()
optimizer.step()
avg_loss = avg_loss + loss.item()
if (i + 1) % print_freq == 0:
print('Epoch {:d} | Batch {:d}/{:d} | Loss {:f}'.format(epoch, i+1, len(base_loader), avg_loss/float(i+1)))
model.eval()
with torch.no_grad():
acc = model.test_loop(val_loader)
if acc > max_acc:
print("Best model! save...")
max_acc = acc
outfile = os.path.join(params.checkpoint_dir, 'best_model.tar')
torch.save({'epoch':epoch, 'state':model.state_dict()}, outfile)
else:
print("GG! Best accuracy {:f}".format(max_acc))
if ((epoch+1) % params.save_freq == 0) or (epoch == stop_epoch-1):
outfile = os.path.join(params.checkpoint_dir, '{:d}.tar'.format(epoch))
torch.save({'epoch': epoch, 'state': model.state_dict()}, outfile)
return model
# --- main function ---
if __name__=='__main__':
# set numpy random seed
np.random.seed(10)
# parser argument
params = parse_args()
print('--- Training ---\n')
print(params)
# output and tensorboard dir
params.checkpoint_dir = '%s/checkpoints/%s'%(params.save_dir, params.name)
if not os.path.isdir(params.checkpoint_dir):
os.makedirs(params.checkpoint_dir)
# dataloader
print('\n--- Prepare dataloader ---')
print('\ttrain with single seen domain {}'.format(params.dataset))
print('\tval with single seen domain {}'.format(params.testset))
base_file = os.path.join(params.data_dir, params.dataset, 'base.json')
val_file = os.path.join(params.data_dir, params.testset, 'val.json')
# model
image_size = 224
n_query = max(1, int(16*params.test_n_way/params.train_n_way))
base_datamgr = SetDataManager(image_size, n_query=n_query, n_way=params.train_n_way, n_support=params.n_shot)
base_loader = base_datamgr.get_data_loader(base_file, aug=params.train_aug)
val_datamgr = SetDataManager(image_size, n_query=n_query, n_way=params.test_n_way, n_support=params.n_shot)
val_loader = val_datamgr.get_data_loader(val_file, aug=False)
if params.method == 'MatchingNet':
model = MatchingNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'RelationNet':
model = RelationNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'ProtoNet':
model = ProtoNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'GNN':
model = GnnNet(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
elif params.method == 'TPN':
model = TPN(model_dict[params.model], n_way=params.train_n_way, n_support=params.n_shot).cuda()
else:
print("Please specify the method!")
assert(False)
model.n_query = n_query
# load model
start_epoch = params.start_epoch
stop_epoch = params.stop_epoch
if params.resume_epoch > 0:
resume_file = os.path.join(params.checkpoint_dir, '{:d}.tar'.format(params.resume_epoch))
tmp = torch.load(resume_file)
start_epoch = tmp['epoch']+1
model.load_state_dict(tmp['state'])
print('\tResume the training weight at {} epoch.'.format(start_epoch))
else:
path = '%s/checkpoints/%s/399.tar' % (params.save_dir, params.resume_dir)
state = torch.load(path)['state']
model_params = model.state_dict()
pretrained_dict = {k: v for k, v in state.items() if k in model_params}
print(pretrained_dict.keys())
model_params.update(pretrained_dict)
model.load_state_dict(model_params)
# training
print('\n--- start the training ---')
model = train(base_loader, val_loader, model, start_epoch, stop_epoch, params)