def test_trainer():
trainer = MyTrainer(Params())
trainer.params.eidx = 3
fn = trainer.save_keypoint()
trainer.train()
assert trainer.params.eidx == trainer.params.epoch
trainer.load_checkpoint(fn)
assert trainer.params.eidx == 3
from torch.optim import SGD
self.model = MyModel()
self.optim = SGD(self.model.parameters(), lr=params.lr)
self.cross = nn.CrossEntropyLoss()
def train_batch(self, eidx, idx, global_step, batch_data, params, device):
optim, cross = self.optim, self.cross
meter = Meter()
xs, ys = batch_data
# 训练逻辑
logits = self.model(xs)
meter.loss = cross(logits, ys)
# 反向传播
meter.loss.backward()
optim.step()
optim.zero_grad()
return meter
params = Params()
params.epoch = 5
params.lr = 0.1
params.dataset = 'svhn'
trainer = MyTrainer(params)
trainer.train()
def get_params():
p = Params()
p.git_commit = False
return p
"""
"""
from thexp import Params
params = Params()
print(params)
params.epoch = 400
params.batch_size = 25
print(params)
from thexp import Params
class MyParams(Params):
def __init__(self):
super().__init__()
self.batch_size = 50
self.topk = (1, 2, 3, 4)
self.optim = dict(lr=0.009, moment=0.9)
params = MyParams()
print(params)
from thexp import Params
params = Params()
params.choice("dataset", "mnist", "cifar10", "cifar100", "svhn")
params.arange("thresh", 5, 0, 20)
print(params)
For commercial projects that require the ability to distribute
the code of this program as part of a program that cannot be
distributed under the GNU General Public License, please contact
[email protected]
to purchase a commercial license.
"""
from thexp import Trainer,Params
import random
class myTrainer(Trainer):
pass
trainer = myTrainer(Params())
for i in range(50):
trainer.logger.info(i)
for i in range(20):
trainer.writter.add_scalar("test",random.random(),i)
# trainer.saver
# trainer.rnd
# ======================================
import torch.nn as nn
class MyModel(nn.Module):