def main(opt):
if not os.path.isdir(opt['log.exp_dir']):
os.makedirs(opt['log.exp_dir'])
# save opts
with open(os.path.join(opt['log.exp_dir'], 'opt.json'), 'w') as f:
json.dump(opt, f)
f.write('\n')
trace_file = os.path.join(opt['log.exp_dir'], 'trace.txt')
# Postprocess arguments
opt['model.x_dim'] = list(map(int, opt['model.x_dim'].split(',')))
opt['log.fields'] = opt['log.fields'].split(',')
np.random.seed(4321)
torch.manual_seed(1234)
if opt['data.cuda']:
torch.cuda.manual_seed(1234)
if opt['data.trainval']:
data = data_utils.load(opt, ['trainval'])
train_loader = data['trainval']
val_loader = None
else:
data = data_utils.load(opt, ['train', 'val'])
train_loader = data['train']
val_loader = data['val']
model = model_utils.load(opt)
if opt['data.cuda']:
model.cuda()
engine = Engine()
meters = { 'train': { field: tnt.meter.AverageValueMeter() for field in opt['log.fields'] } }
if val_loader is not None:
meters['val'] = { field: tnt.meter.AverageValueMeter() for field in opt['log.fields'] }
def on_start(state):
if os.path.isfile(trace_file):
os.remove(trace_file)
state['scheduler'] = lr_scheduler.StepLR(state['optimizer'], opt['train.decay_every'], gamma=1)
engine.hooks['on_start'] = on_start
def on_start_epoch(state):
for split, split_meters in meters.items():
for field, meter in split_meters.items():
meter.reset()
state['scheduler'].step()
engine.hooks['on_start_epoch'] = on_start_epoch
def on_update(state):
for field, meter in meters['train'].items():
meter.add(state['output'][field])
engine.hooks['on_update'] = on_update
def on_end_epoch(hook_state, state):
if val_loader is not None:
if 'best_loss' not in hook_state:
hook_state['best_loss'] = np.inf
if 'wait' not in hook_state:
hook_state['wait'] = 0
if val_loader is not None:
model_utils.evaluate(state['model'],
val_loader,
meters['val'],
desc="Epoch {:d} valid".format(state['epoch']))
meter_vals = log_utils.extract_meter_values(meters)
print("Epoch {:02d}: {:s}".format(state['epoch'], log_utils.render_meter_values(meter_vals)))
meter_vals['epoch'] = state['epoch']
with open(trace_file, 'a') as f:
json.dump(meter_vals, f)
f.write('\n')
if val_loader is not None and False: # disable this block
if meter_vals['val']['loss'] < hook_state['best_loss']:
hook_state['best_loss'] = meter_vals['val']['loss']
print("==> best model (loss = {:0.6f}), saving model...".format(hook_state['best_loss']))
state['model'].cpu()
torch.save(state['model'], os.path.join(opt['log.exp_dir'], 'best_model.pt'))
if opt['data.cuda']:
state['model'].cuda()
hook_state['wait'] = 0
else:
hook_state['wait'] += 1
if hook_state['wait'] > opt['train.patience']:
print("==> patience {:d} exceeded".format(opt['train.patience']))
state['stop'] = True
else:
state['model'].cpu()
torch.save(state['model'], os.path.join(opt['log.exp_dir'], 'best_model.pt'))
if opt['data.cuda']:
state['model'].cuda()
engine.hooks['on_end_epoch'] = partial(on_end_epoch, { })
engine.train(
model = model,
loader = train_loader,
optim_method = getattr(optim, opt['train.optim_method']),
optim_config = { 'lr': opt['train.learning_rate'],
'weight_decay': opt['train.weight_decay'] },
max_epoch = opt['train.epochs']
)
def main(opt):
if not os.path.isdir(opt['log.exp_dir']):
os.makedirs(opt['log.exp_dir'])
# save opts
with open(os.path.join(opt['log.exp_dir'], 'opt.json'), 'w') as f:
json.dump(opt, f)
f.write('\n')
trace_file = os.path.join(opt['log.exp_dir'], 'trace.txt')
# Postprocess arguments
opt['model.x_dim'] = list(map(int, opt['model.x_dim'].split(',')))
opt['log.fields'] = opt['log.fields'].split(',')
torch.manual_seed(1234)
if opt['data.cuda']:
torch.cuda.manual_seed(1234)
#if opt['data.trainval']:
# data = data_utils.load(opt, ['trainval'])
# train_loader = data['trainval']
# val_loader = None
#else:
# data = data_utils.load(opt, ['train', 'val'])
# train_loader = data['train']
# val_loader = data['val']
val_loader = 'HACK'
# added
train_way = opt['data.way']
train_shot = opt['data.shot']
train_query = opt['data.query']
train_episodes = opt['data.train_episodes']
data = PytorchBirdsDataLoader(n_episodes=train_episodes,
n_way=train_way,
n_query=train_query,
n_support=train_shot,
image_dim=(224, 224, 3))
model = model_utils.load(opt)
if opt['data.cuda']:
model.cuda()
#model.encoder.cuda()
engine = Engine()
meters = {
'train':
{field: tnt.meter.AverageValueMeter()
for field in opt['log.fields']}
}
if val_loader is not None:
meters['val'] = {
field: tnt.meter.AverageValueMeter()
for field in opt['log.fields']
}
def on_start(state):
if os.path.isfile(trace_file):
os.remove(trace_file)
state['scheduler'] = lr_scheduler.StepLR(state['optimizer'],
opt['train.decay_every'],
gamma=0.5)
engine.hooks['on_start'] = on_start
def on_start_epoch(state):
state['loader'].mode = 'train' # added
for split, split_meters in meters.items():
for field, meter in split_meters.items():
meter.reset()
state['scheduler'].step()
engine.hooks['on_start_epoch'] = on_start_epoch
def on_update(state):
for field, meter in meters['train'].items():
meter.add(state['output'][field])
engine.hooks['on_update'] = on_update
def on_end_epoch(hook_state, state):
if val_loader is not None:
if 'best_loss' not in hook_state:
hook_state['best_loss'] = np.inf
if 'wait' not in hook_state:
hook_state['wait'] = 0
if val_loader is not None:
state['loader'].mode = 'val' # added
model_utils.evaluate(
state['model'],
state['loader'], #val_loader,
meters['val'],
desc="Epoch {:d} valid".format(state['epoch']))
meter_vals = log_utils.extract_meter_values(meters)
print("Epoch {:02d}: {:s}".format(
state['epoch'], log_utils.render_meter_values(meter_vals)))
meter_vals['epoch'] = state['epoch']
# this prevents cuda bugs
meter_vals = {
k: ({kk: float(vv)
for kk, vv in v.items()} if isinstance(v, dict) else v)
for k, v in meter_vals.items()
}
with open(trace_file, 'a') as f:
json.dump(meter_vals, f)
f.write('\n')
if val_loader is not None:
if meter_vals['val']['loss'] < hook_state['best_loss']:
hook_state['best_loss'] = meter_vals['val']['loss']
print(
"==> best model (loss = {:0.6f}), saving model...".format(
hook_state['best_loss']))
state['model'].cpu()
# used with inception
#torch.save(state['model'].encoder.added_layers, os.path.join(opt['log.exp_dir'], 'best_model.t7'))
torch.save(state['model'],
os.path.join(opt['log.exp_dir'], 'best_model.t7'))
if opt['data.cuda']:
state['model'].cuda()
hook_state['wait'] = 0
else:
hook_state['wait'] += 1
if hook_state['wait'] > opt['train.patience']:
print("==> patience {:d} exceeded".format(
opt['train.patience']))
state['stop'] = True
else:
state['model'].cpu()
# used with inception
#torch.save(state['model'].encoder.added_layers, os.path.join(opt['log.exp_dir'], 'best_model.t7'))
torch.save(state['model'],
os.path.join(opt['log.exp_dir'], 'best_model.t7'))
if opt['data.cuda']:
state['model'].cuda()
engine.hooks['on_end_epoch'] = partial(on_end_epoch, {})
engine.train(
model=model,
loader=data, #train_loader,
optim_method=getattr(optim, opt['train.optim_method']),
optim_config={
'lr': opt['train.learning_rate'],
'weight_decay': opt['train.weight_decay']
},
max_epoch=opt['train.epochs'])
def main(opt):
# 新建日志目录
if not os.path.isdir(opt['log.exp_dir']):
os.makedirs(opt['log.exp_dir'])
# save opts
with open(os.path.join(opt['log.exp_dir'], 'opt.json'), 'w') as f:
json.dump(opt, f)
f.write('\n')
trace_file = os.path.join(opt['log.exp_dir'], 'trace.txt')
# Postprocess arguments
opt['model.x_dim'] = list(map(int, opt['model.x_dim'].split(',')))
opt['log.fields'] = opt['log.fields'].split(',')
torch.manual_seed(1234)
if opt['data.cuda']:
torch.cuda.manual_seed(1234)
#??? trainval是什么???
if opt['data.trainval']:
data = data_utils.load(opt, ['trainval'])
train_loader = data['trainval']
val_loader = None
else:
data = data_utils.load(opt, ['train', 'val'])
train_loader = data['train']
val_loader = data['val']
model = model_utils.load(opt)
#model = torch.load("results/m5_5way5shot/pre.t7")
if opt['data.cuda']:
model.cuda()
engine = Engine()
meters = {
'train':
{field: tnt.meter.AverageValueMeter()
for field in opt['log.fields']}
}
if val_loader is not None:
meters['val'] = {
field: tnt.meter.AverageValueMeter()
for field in opt['log.fields']
}
# 看名字知道功能的start函数，配置优化器
def on_start(state):
if os.path.isfile(trace_file):
os.remove(trace_file)
state['scheduler'] = lr_scheduler.StepLR(state['optimizer'],
opt['train.decay_every'],
gamma=0.5)
engine.hooks['on_start'] = on_start
# 第一个epoch需要解决的事
def on_start_epoch(state):
for split, split_meters in meters.items():
for field, meter in split_meters.items():
meter.reset()
state['scheduler'].step()
engine.hooks['on_start_epoch'] = on_start_epoch
# 更新那个算平均的类
def on_update(state):
for field, meter in meters['train'].items():
meter.add(state['output'][field])
engine.hooks['on_update'] = on_update
#一个epoch结束时判断训练效果，以及是否结束训练(patience?为什么不用loss的改变?看了实际训练貌似loss变化挺大的)
title = '%s, %s: %i_%iw_%is' % (opt['model.exp_name'], opt['data.dataset'],
opt['data.way'], opt['data.test_way'],
opt['data.test_shot'])
lossPic = visual_utils.train_val_loss(title)
accPic = visual_utils.train_val_acc(title)
def on_end_epoch(hook_state, state):
if val_loader is not None:
if 'best_loss' not in hook_state:
hook_state['best_loss'] = np.inf
if 'wait' not in hook_state:
hook_state['wait'] = 0
if val_loader is not None:
model_utils.evaluate(state['model'],
val_loader,
meters['val'],
desc="Epoch {:d} valid".format(
state['epoch']))
meter_vals = log_utils.extract_meter_values(meters)
lossPic(state['epoch'], meter_vals['train']['loss'],
meter_vals['val']['loss'])
accPic(state['epoch'], meter_vals['train']['acc'],
meter_vals['val']['acc'])
print("Epoch {:02d}: {:s}".format(
state['epoch'], log_utils.render_meter_values(meter_vals)))
meter_vals['epoch'] = state['epoch']
with open(trace_file, 'a') as f:
json.dump(meter_vals, f)
f.write('\n')
if val_loader is not None:
if meter_vals['val']['loss'] < hook_state['best_loss']:
hook_state['best_loss'] = meter_vals['val']['loss']
print(
"==> best model (loss = {:0.6f}), saving model...".format(
hook_state['best_loss']))
state['model'].cpu()
torch.save(state['model'],
os.path.join(opt['log.exp_dir'], 'best_model.t7'))
if opt['data.cuda']:
state['model'].cuda()
hook_state['wait'] = 0
else:
hook_state['wait'] += 1
if hook_state['wait'] > opt['train.patience']:
print("==> patience {:d} exceeded".format(
opt['train.patience']))
state['stop'] = True
else:
state['model'].cpu()
torch.save(state['model'],
os.path.join(opt['log.exp_dir'], 'best_model.t7'))
if opt['data.cuda']:
state['model'].cuda()
engine.hooks['on_end_epoch'] = partial(on_end_epoch, {})
engine.train(model=model,
loader=train_loader,
optim_method=getattr(optim, opt['train.optim_method']),
optim_config={
'lr': opt['train.learning_rate'],
'weight_decay': opt['train.weight_decay']
},
max_epoch=opt['train.epochs'])
def main(opt):
if not os.path.isdir(opt['log.exp_dir']):
os.makedirs(opt['log.exp_dir'])
# save opts
# 将opts加入文件中
with open(os.path.join(opt['log.exp_dir'], 'opt.json'), 'w') as f:
json.dump(opt, f)
f.write('\n')
trace_file = os.path.join(opt['log.exp_dir'], 'trace.txt')
# Postprocess arguments
opt['model.x_dim'] = list(map(int, opt['model.x_dim'].split(',')))
opt['log.fields'] = opt['log.fields'].split(',')
torch.manual_seed(1234)
if opt['data.cuda']:
torch.cuda.manual_seed(1234)
# 加载数据
if opt['data.trainval']:
# load Omniglot dataset
data = data_utils.load(opt, ['trainval'])
train_loader = data['trainval']
val_loader = None
else:
data = data_utils.load(opt, ['train', 'val'])
train_loader = data['train']
val_loader = data['val']
# 使用模型相关参数加载模型
model = model_utils.load(opt)
if opt['data.cuda']:
model.cuda()
engine = Engine()
# torchnet.meter评估方法性能，这里用的平均值
# 先建立一个每个指标及其对应评价值的字典
meters = { 'train': { field: tnt.meter.AverageValueMeter() for field in opt['log.fields'] } }
if val_loader is not None:
meters['val'] = { field: tnt.meter.AverageValueMeter() for field in opt['log.fields'] }
def on_start(state):
if os.path.isfile(trace_file):
os.remove(trace_file)
# 定义学习率衰减机制
state['scheduler'] = lr_scheduler.StepLR(state['optimizer'], opt['train.decay_every'], gamma=0.5)
# 将上面的函数赋给匿名函数engine.hooks
engine.hooks['on_start'] = on_start
# 每个epoch开始时
def on_start_epoch(state):
for split, split_meters in meters.items():
for field, meter in split_meters.items():
# 重置评价指标
meter.reset()
# 调用optimizer的step()函数进行回传
state['scheduler'].step()
# 用匿名函数包装
engine.hooks['on_start_epoch'] = on_start_epoch
# 更新评价指标
def on_update(state):
# 对于所有训练指标，更新每个训练指标的值
for field, meter in meters['train'].items():
meter.add(state['output'][field])
engine.hooks['on_update'] = on_update
# 在每个epoch结束时，给出这个epoch的评价值
def on_end_epoch(hook_state, state):
if val_loader is not None:
if 'best_loss' not in hook_state:
hook_state['best_loss'] = np.inf
if 'wait' not in hook_state:
hook_state['wait'] = 0
if val_loader is not None:
# 使用val评价模型
model_utils.evaluate(state['model'],
val_loader,
meters['val'],
desc="Epoch {:d} valid".format(state['epoch']))
meter_vals = log_utils.extract_meter_values(meters)
print("Epoch {:02d}: {:s}".format(state['epoch'], log_utils.render_meter_values(meter_vals)))
meter_vals['epoch'] = state['epoch']
with open(trace_file, 'a') as f:
json.dump(meter_vals, f)
f.write('\n')
# 更新目前最好的loss
if val_loader is not None:
if meter_vals['val']['loss'] < hook_state['best_loss']:
hook_state['best_loss'] = meter_vals['val']['loss']
print("==> best model (loss = {:0.6f}), saving model...".format(hook_state['best_loss']))
state['model'].cpu()
torch.save(state['model'], os.path.join(opt['log.exp_dir'], 'best_model.pt'))
if opt['data.cuda']:
state['model'].cuda()
hook_state['wait'] = 0
# early stop
else:
hook_state['wait'] += 1
if hook_state['wait'] > opt['train.patience']:
print("==> patience {:d} exceeded".format(opt['train.patience']))
state['stop'] = True