mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-12 12:40:20 +08:00
adding tests
This commit is contained in:
William Falcon
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from .lightning_module_template import LightningTemplateModel
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import os
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from collections import OrderedDict
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import torch.nn as nn
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from torchvision.datasets import MNIST
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import torchvision.transforms as transforms
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import torch
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import torch.nn.functional as F
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from test_tube import HyperOptArgumentParser
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from torch import optim
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from pytorch_lightning.root_module.root_module import LightningModule
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class LightningTemplateModel(LightningModule):
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"""
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Sample model to show how to define a template
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"""
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def __init__(self, hparams):
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"""
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Pass in parsed HyperOptArgumentParser to the model
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:param hparams:
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"""
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# init superclass
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super(LightningTemplateModel, self).__init__(hparams)
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self.batch_size = hparams.batch_size
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# build model
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self.__build_model()
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# ---------------------
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# MODEL SETUP
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# ---------------------
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def __build_model(self):
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"""
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Layout model
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:return:
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"""
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self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
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self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
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self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
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self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
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# ---------------------
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# TRAINING
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# ---------------------
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def forward(self, x):
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"""
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No special modification required for lightning, define as you normally would
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:param x:
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:return:
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"""
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x = self.c_d1(x)
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x = torch.tanh(x)
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x = self.c_d1_bn(x)
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x = self.c_d1_drop(x)
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x = self.c_d2(x)
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logits = F.log_softmax(x, dim=1)
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return logits
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def loss(self, labels, logits):
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nll = F.nll_loss(logits, labels)
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return nll
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def training_step(self, data_batch, batch_i):
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"""
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Lightning calls this inside the training loop
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:param data_batch:
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:return:
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"""
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# forward pass
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x, y = data_batch
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x = x.view(x.size(0), -1)
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y_hat = self.forward(x)
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# calculate loss
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loss_val = self.loss(y, y_hat)
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output = OrderedDict({
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'loss': loss_val
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})
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# can also return just a scalar instead of a dict (return loss_val)
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return output
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def validation_step(self, data_batch, batch_i):
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"""
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Lightning calls this inside the validation loop
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:param data_batch:
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:return:
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"""
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x, y = data_batch
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x = x.view(x.size(0), -1)
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y_hat = self.forward(x)
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loss_val = self.loss(y, y_hat)
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# acc
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labels_hat = torch.argmax(y_hat, dim=1)
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val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': torch.tensor(val_acc).cuda(loss_val.device.index),
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})
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# can also return just a scalar instead of a dict (return loss_val)
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return output
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def validation_end(self, outputs):
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"""
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Called at the end of validation to aggregate outputs
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:param outputs: list of individual outputs of each validation step
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:return:
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"""
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# if returned a scalar from validation_step, outputs is a list of tensor scalars
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# we return just the average in this case (if we want)
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# return torch.stack(outputs).mean()
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val_loss_mean = 0
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val_acc_mean = 0
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for output in outputs:
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val_loss_mean += output['val_loss']
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val_acc_mean += output['val_acc']
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val_loss_mean /= len(outputs)
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val_acc_mean /= len(outputs)
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tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
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return tqdm_dic
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def update_tng_log_metrics(self, logs):
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return logs
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# ---------------------
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# MODEL SAVING
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# ---------------------
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def get_save_dict(self):
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checkpoint = {'state_dict': self.state_dict()}
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return checkpoint
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def load_model_specific(self, checkpoint):
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self.load_state_dict(checkpoint['state_dict'])
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pass
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# ---------------------
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# TRAINING SETUP
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# ---------------------
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def configure_optimizers(self):
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"""
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return whatever optimizers we want here
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:return: list of optimizers
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"""
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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return [optimizer]
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def __dataloader(self, train):
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# init data generators
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transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
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# when using multi-node we need to add the datasampler
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train_sampler = None
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batch_size = self.hparams.batch_size
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try:
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if self.on_gpu:
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train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)
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batch_size = batch_size // self.trainer.world_size # scale batch size
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except Exception as e:
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pass
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should_shuffle = train_sampler is None
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loader = DataLoader(
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dataset=dataset,
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batch_size=batch_size,
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shuffle=should_shuffle,
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sampler=train_sampler
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)
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return loader
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@property
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def tng_dataloader(self):
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if self._tng_dataloader is None:
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try:
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self._tng_dataloader = self.__dataloader(train=True)
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except Exception as e:
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print(e)
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raise e
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return self._tng_dataloader
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@property
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def val_dataloader(self):
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if self._val_dataloader is None:
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try:
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self._val_dataloader = self.__dataloader(train=False)
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except Exception as e:
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print(e)
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raise e
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return self._val_dataloader
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@property
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def test_dataloader(self):
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if self._test_dataloader is None:
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try:
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self._test_dataloader = self.__dataloader(train=False)
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except Exception as e:
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print(e)
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raise e
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return self._test_dataloader
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@staticmethod
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def add_model_specific_args(parent_parser, root_dir):
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"""
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Parameters you define here will be available to your model through self.hparams
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:param parent_parser:
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:param root_dir:
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:return:
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"""
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parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
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# param overwrites
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# parser.set_defaults(gradient_clip=5.0)
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# network params
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parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
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parser.add_argument('--in_features', default=28*28, type=int)
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parser.add_argument('--out_features', default=10, type=int)
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parser.add_argument('--hidden_dim', default=50000, type=int) # use 500 for CPU, 50000 for GPU to see speed difference
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# data
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parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
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# training params (opt)
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parser.opt_list('--learning_rate', default=0.001*8, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
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tunable=False)
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parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
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# if using 2 nodes with 4 gpus each the batch size here (256) will be 256 / (2*8) = 16 per gpu
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parser.opt_list('--batch_size', default=256*8, type=int, options=[32, 64, 128, 256], tunable=False,
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help='batch size will be divided over all the gpus being used across all nodes')
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return parser
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@@ -1,172 +0,0 @@
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import os
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import sys
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import numpy as np
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from time import sleep
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import torch
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from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
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from pytorch_lightning.models.trainer import Trainer
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from pytorch_lightning.utils.arg_parse import add_default_args
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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# ---------------------
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# DEFINE MODEL HERE
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# ---------------------
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from lightning_module_template import LightningTemplateModel
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# ---------------------
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"""
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Allows training by using command line arguments
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Run by:
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# TYPE YOUR RUN COMMAND HERE
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"""
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def main_local(hparams):
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main(hparams, None, None)
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def main(hparams, cluster, results_dict):
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"""
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Main training routine specific for this project
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:param hparams:
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:return:
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"""
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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print('loading model...')
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model = LightningTemplateModel(hparams)
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print('model built')
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# ------------------------
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# 2 INIT TEST TUBE EXP
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# ------------------------
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# when using grid search, it's possible for all models to start at once
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# and use the same test tube experiment version
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relative_node_id = int(os.environ['SLURM_NODEID'])
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sleep(relative_node_id + 1)
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# init experiment
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exp = Experiment(
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name=hyperparams.experiment_name,
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save_dir=hyperparams.test_tube_save_path,
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autosave=False,
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description='test demo'
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)
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exp.argparse(hparams)
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exp.save()
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# ------------------------
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# 3 DEFINE CALLBACKS
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# ------------------------
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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early_stop = EarlyStopping(
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monitor='val_acc',
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patience=3,
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verbose=True,
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mode='max'
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)
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_best_only=True,
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verbose=True,
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monitor='val_loss',
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mode='min'
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)
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# ------------------------
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# 4 INIT TRAINER
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# ------------------------
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trainer = Trainer(
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experiment=exp,
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cluster=cluster,
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop,
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gpus=hparams.gpus,
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nb_gpu_nodes=hyperparams.nb_gpu_nodes
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)
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# ------------------------
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# 5 START TRAINING
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# ------------------------
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trainer.fit(model)
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def optimize_on_cluster(hyperparams):
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# enable cluster training
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# log all scripts to the test tube folder
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cluster = SlurmCluster(
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hyperparam_optimizer=hyperparams,
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log_path=hyperparams.slurm_log_path,
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)
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# email for cluster coms
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cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
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# configure cluster
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cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
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cluster.per_experiment_nb_nodes = hyperparams.nb_gpu_nodes
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cluster.job_time = '2:00:00'
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cluster.gpu_type = 'volta'
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cluster.memory_mb_per_node = 0
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# any modules for code to run in env
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cluster.add_command('source activate lightning')
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# run only on 32GB voltas
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cluster.add_slurm_cmd(cmd='constraint', value='volta32gb', comment='use 32gb gpus')
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cluster.add_slurm_cmd(cmd='partition', value=hyperparams.gpu_partition, comment='use 32gb gpus')
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# run hopt
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# creates and submits jobs to slurm
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cluster.optimize_parallel_cluster_gpu(
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main,
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nb_trials=hyperparams.nb_hopt_trials,
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job_name=hyperparams.experiment_name
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)
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if __name__ == '__main__':
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# use default args
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root_dir = os.path.dirname(os.path.realpath(__file__))
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demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
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checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
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test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
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slurm_out_dir = os.path.join(demo_log_dir, 'slurm_scripts')
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parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
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# cluster args not defined inside the model
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parent_parser.add_argument('--gpu_partition', type=str, help='consult your cluster manual')
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# TODO: make 1 param
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parent_parser.add_argument('--per_experiment_nb_gpus', type=int, help='how many gpus to use in a node')
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parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node')
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parent_parser.add_argument('--nb_gpu_nodes', type=int, default=1, help='how many nodes to use in a cluster')
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parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
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parent_parser.add_argument('--slurm_log_path', type=str, default=slurm_out_dir, help='where to save slurm meta')
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parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
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parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
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parent_parser.add_argument('--nb_hopt_trials', type=int, default=1, help='how many grid search trials to run')
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# allow model to overwrite or extend args
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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# run on HPC cluster
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print('RUNNING ON SLURM CLUSTER')
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optimize_on_cluster(hyperparams)
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@@ -1,110 +0,0 @@
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"""
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Runs a model on a single node across N-gpus.
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"""
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import os
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import sys
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import numpy as np
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from time import sleep
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import torch
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from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
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from pytorch_lightning.models.trainer import Trainer
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from pytorch_lightning.utils.arg_parse import add_default_args
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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from lightning_module_template import LightningTemplateModel
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def main(hparams):
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"""
|
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Main training routine specific for this project
|
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:param hparams:
|
||||
:return:
|
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"""
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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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print('loading model...')
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model = LightningTemplateModel(hparams)
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print('model built')
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# ------------------------
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# 2 INIT TEST TUBE EXP
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# ------------------------
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# init experiment
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exp = Experiment(
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name=hyperparams.experiment_name,
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save_dir=hyperparams.test_tube_save_path,
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autosave=False,
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description='test demo'
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)
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exp.argparse(hparams)
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exp.save()
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# ------------------------
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# 3 DEFINE CALLBACKS
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# ------------------------
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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early_stop = EarlyStopping(
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monitor='val_acc',
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patience=3,
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verbose=True,
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mode='max'
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)
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_best_only=True,
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verbose=True,
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monitor='val_loss',
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mode='min'
|
||||
)
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# ------------------------
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# 4 INIT TRAINER
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||||
# ------------------------
|
||||
trainer = Trainer(
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experiment=exp,
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||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 5 START TRAINING
|
||||
# ------------------------
|
||||
trainer.fit(model)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# dirs
|
||||
root_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
|
||||
checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
|
||||
test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
|
||||
|
||||
# although we user hyperOptParser, we are using it only as argparse right now
|
||||
parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
|
||||
|
||||
# gpu args
|
||||
parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
|
||||
parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
|
||||
parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
|
||||
|
||||
# allow model to overwrite or extend args
|
||||
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
|
||||
hyperparams = parser.parse_args()
|
||||
|
||||
# ---------------------
|
||||
# RUN TRAINING
|
||||
# ---------------------
|
||||
# run on HPC cluster
|
||||
print(f'RUNNING ON CPU')
|
||||
main(hyperparams)
|
||||
@@ -1,113 +0,0 @@
|
||||
"""
|
||||
Runs a model on a single node across N-gpus.
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
from time import sleep
|
||||
import torch
|
||||
|
||||
from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
|
||||
from pytorch_lightning.models.trainer import Trainer
|
||||
from pytorch_lightning.utils.arg_parse import add_default_args
|
||||
|
||||
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
|
||||
|
||||
SEED = 2334
|
||||
torch.manual_seed(SEED)
|
||||
np.random.seed(SEED)
|
||||
|
||||
from lightning_module_template import LightningTemplateModel
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
:param hparams:
|
||||
:return:
|
||||
"""
|
||||
# ------------------------
|
||||
# 1 INIT LIGHTNING MODEL
|
||||
# ------------------------
|
||||
print('loading model...')
|
||||
model = LightningTemplateModel(hparams)
|
||||
print('model built')
|
||||
|
||||
# ------------------------
|
||||
# 2 INIT TEST TUBE EXP
|
||||
# ------------------------
|
||||
|
||||
# init experiment
|
||||
exp = Experiment(
|
||||
name=hyperparams.experiment_name,
|
||||
save_dir=hyperparams.test_tube_save_path,
|
||||
autosave=False,
|
||||
description='test demo'
|
||||
)
|
||||
|
||||
exp.argparse(hparams)
|
||||
exp.save()
|
||||
|
||||
# ------------------------
|
||||
# 3 DEFINE CALLBACKS
|
||||
# ------------------------
|
||||
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
|
||||
early_stop = EarlyStopping(
|
||||
monitor='val_acc',
|
||||
patience=3,
|
||||
verbose=True,
|
||||
mode='max'
|
||||
)
|
||||
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath=model_save_path,
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='val_loss',
|
||||
mode='min'
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 4 INIT TRAINER
|
||||
# ------------------------
|
||||
trainer = Trainer(
|
||||
experiment=exp,
|
||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
gpus=hparams.gpus,
|
||||
use_amp=True
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 5 START TRAINING
|
||||
# ------------------------
|
||||
trainer.fit(model)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# dirs
|
||||
root_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
|
||||
checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
|
||||
test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
|
||||
|
||||
# although we user hyperOptParser, we are using it only as argparse right now
|
||||
parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
|
||||
|
||||
# gpu args
|
||||
parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
|
||||
parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
|
||||
parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
|
||||
parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
|
||||
|
||||
# allow model to overwrite or extend args
|
||||
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
|
||||
hyperparams = parser.parse_args()
|
||||
|
||||
# ---------------------
|
||||
# RUN TRAINING
|
||||
# ---------------------
|
||||
# run on HPC cluster
|
||||
print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
|
||||
main(hyperparams)
|
||||
@@ -1,112 +0,0 @@
|
||||
"""
|
||||
Runs a model on a single node across N-gpus.
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
from time import sleep
|
||||
import torch
|
||||
|
||||
from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
|
||||
from pytorch_lightning.models.trainer import Trainer
|
||||
from pytorch_lightning.utils.arg_parse import add_default_args
|
||||
|
||||
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
|
||||
|
||||
SEED = 2334
|
||||
torch.manual_seed(SEED)
|
||||
np.random.seed(SEED)
|
||||
|
||||
from lightning_module_template import LightningTemplateModel
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
:param hparams:
|
||||
:return:
|
||||
"""
|
||||
# ------------------------
|
||||
# 1 INIT LIGHTNING MODEL
|
||||
# ------------------------
|
||||
print('loading model...')
|
||||
model = LightningTemplateModel(hparams)
|
||||
print('model built')
|
||||
|
||||
# ------------------------
|
||||
# 2 INIT TEST TUBE EXP
|
||||
# ------------------------
|
||||
|
||||
# init experiment
|
||||
exp = Experiment(
|
||||
name=hyperparams.experiment_name,
|
||||
save_dir=hyperparams.test_tube_save_path,
|
||||
autosave=False,
|
||||
description='test demo'
|
||||
)
|
||||
|
||||
exp.argparse(hparams)
|
||||
exp.save()
|
||||
|
||||
# ------------------------
|
||||
# 3 DEFINE CALLBACKS
|
||||
# ------------------------
|
||||
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
|
||||
early_stop = EarlyStopping(
|
||||
monitor='val_acc',
|
||||
patience=3,
|
||||
verbose=True,
|
||||
mode='max'
|
||||
)
|
||||
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath=model_save_path,
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='val_loss',
|
||||
mode='min'
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 4 INIT TRAINER
|
||||
# ------------------------
|
||||
trainer = Trainer(
|
||||
experiment=exp,
|
||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
gpus=hparams.gpus,
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 5 START TRAINING
|
||||
# ------------------------
|
||||
trainer.fit(model)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# dirs
|
||||
root_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
|
||||
checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
|
||||
test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
|
||||
|
||||
# although we user hyperOptParser, we are using it only as argparse right now
|
||||
parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
|
||||
|
||||
# gpu args
|
||||
parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
|
||||
parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
|
||||
parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
|
||||
parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
|
||||
|
||||
# allow model to overwrite or extend args
|
||||
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
|
||||
hyperparams = parser.parse_args()
|
||||
|
||||
# ---------------------
|
||||
# RUN TRAINING
|
||||
# ---------------------
|
||||
# run on HPC cluster
|
||||
print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
|
||||
main(hyperparams)
|
||||
@@ -1,112 +0,0 @@
|
||||
"""
|
||||
Runs a model on a single node across N-gpus.
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
from time import sleep
|
||||
import torch
|
||||
|
||||
from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
|
||||
from pytorch_lightning.models.trainer import Trainer
|
||||
from pytorch_lightning.utils.arg_parse import add_default_args
|
||||
|
||||
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
|
||||
|
||||
SEED = 2334
|
||||
torch.manual_seed(SEED)
|
||||
np.random.seed(SEED)
|
||||
|
||||
from lightning_module_template import LightningTemplateModel
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
:param hparams:
|
||||
:return:
|
||||
"""
|
||||
# ------------------------
|
||||
# 1 INIT LIGHTNING MODEL
|
||||
# ------------------------
|
||||
print('loading model...')
|
||||
model = LightningTemplateModel(hparams)
|
||||
print('model built')
|
||||
|
||||
# ------------------------
|
||||
# 2 INIT TEST TUBE EXP
|
||||
# ------------------------
|
||||
|
||||
# init experiment
|
||||
exp = Experiment(
|
||||
name=hyperparams.experiment_name,
|
||||
save_dir=hyperparams.test_tube_save_path,
|
||||
autosave=False,
|
||||
description='test demo'
|
||||
)
|
||||
|
||||
exp.argparse(hparams)
|
||||
exp.save()
|
||||
|
||||
# ------------------------
|
||||
# 3 DEFINE CALLBACKS
|
||||
# ------------------------
|
||||
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
|
||||
early_stop = EarlyStopping(
|
||||
monitor='val_acc',
|
||||
patience=3,
|
||||
verbose=True,
|
||||
mode='max'
|
||||
)
|
||||
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath=model_save_path,
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='val_loss',
|
||||
mode='min'
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 4 INIT TRAINER
|
||||
# ------------------------
|
||||
trainer = Trainer(
|
||||
experiment=exp,
|
||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
gpus=hparams.gpus,
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 5 START TRAINING
|
||||
# ------------------------
|
||||
trainer.fit(model)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# dirs
|
||||
root_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
|
||||
checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
|
||||
test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
|
||||
|
||||
# although we user hyperOptParser, we are using it only as argparse right now
|
||||
parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
|
||||
|
||||
# gpu args
|
||||
parent_parser.add_argument('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
|
||||
parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
|
||||
parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
|
||||
parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
|
||||
|
||||
# allow model to overwrite or extend args
|
||||
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
|
||||
hyperparams = parser.parse_args()
|
||||
|
||||
# ---------------------
|
||||
# RUN TRAINING
|
||||
# ---------------------
|
||||
# run on HPC cluster
|
||||
print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
|
||||
main(hyperparams)
|
||||
@@ -1,112 +0,0 @@
|
||||
"""
|
||||
Runs a model on a single node across N-gpus.
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import numpy as np
|
||||
from time import sleep
|
||||
import torch
|
||||
|
||||
from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
|
||||
from pytorch_lightning.models.trainer import Trainer
|
||||
from pytorch_lightning.utils.arg_parse import add_default_args
|
||||
|
||||
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
|
||||
|
||||
SEED = 2334
|
||||
torch.manual_seed(SEED)
|
||||
np.random.seed(SEED)
|
||||
|
||||
from lightning_module_template import LightningTemplateModel
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
:param hparams:
|
||||
:return:
|
||||
"""
|
||||
# ------------------------
|
||||
# 1 INIT LIGHTNING MODEL
|
||||
# ------------------------
|
||||
print('loading model...')
|
||||
model = LightningTemplateModel(hparams)
|
||||
print('model built')
|
||||
|
||||
# ------------------------
|
||||
# 2 INIT TEST TUBE EXP
|
||||
# ------------------------
|
||||
|
||||
# init experiment
|
||||
exp = Experiment(
|
||||
name=hyperparams.experiment_name,
|
||||
save_dir=hyperparams.test_tube_save_path,
|
||||
autosave=False,
|
||||
description='test demo'
|
||||
)
|
||||
|
||||
exp.argparse(hparams)
|
||||
exp.save()
|
||||
|
||||
# ------------------------
|
||||
# 3 DEFINE CALLBACKS
|
||||
# ------------------------
|
||||
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
|
||||
early_stop = EarlyStopping(
|
||||
monitor='val_acc',
|
||||
patience=3,
|
||||
verbose=True,
|
||||
mode='max'
|
||||
)
|
||||
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath=model_save_path,
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='val_loss',
|
||||
mode='min'
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 4 INIT TRAINER
|
||||
# ------------------------
|
||||
trainer = Trainer(
|
||||
experiment=exp,
|
||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
gpus=hparams.gpus,
|
||||
)
|
||||
|
||||
# ------------------------
|
||||
# 5 START TRAINING
|
||||
# ------------------------
|
||||
trainer.fit(model)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# dirs
|
||||
root_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
|
||||
checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
|
||||
test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
|
||||
|
||||
# although we user hyperOptParser, we are using it only as argparse right now
|
||||
parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
|
||||
|
||||
# gpu args
|
||||
parent_parser.add_argument('--gpus', type=str, default='0', help='how many gpus to use in the node. -1 uses all the gpus on the node')
|
||||
parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
|
||||
parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
|
||||
parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')
|
||||
|
||||
# allow model to overwrite or extend args
|
||||
parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
|
||||
hyperparams = parser.parse_args()
|
||||
|
||||
# ---------------------
|
||||
# RUN TRAINING
|
||||
# ---------------------
|
||||
# run on HPC cluster
|
||||
print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
|
||||
main(hyperparams)
|
||||
@@ -1,73 +0,0 @@
|
||||
import os
|
||||
import sys
|
||||
|
||||
from test_tube import HyperOptArgumentParser, Experiment
|
||||
from pytorch_lightning.models.trainer import Trainer
|
||||
from pytorch_lightning.utils.arg_parse import add_default_args
|
||||
from pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
|
||||
from docs.source.examples.example_model import ExampleModel
|
||||
|
||||
|
||||
def main(hparams):
|
||||
"""
|
||||
Main training routine specific for this project
|
||||
:param hparams:
|
||||
:return:
|
||||
"""
|
||||
# init experiment
|
||||
exp = Experiment(
|
||||
name=hparams.tt_name,
|
||||
debug=hparams.debug,
|
||||
save_dir=hparams.tt_save_path,
|
||||
version=hparams.hpc_exp_number,
|
||||
autosave=False,
|
||||
description=hparams.tt_description
|
||||
)
|
||||
|
||||
exp.argparse(hparams)
|
||||
exp.save()
|
||||
|
||||
# build model
|
||||
model = ExampleModel(hparams)
|
||||
|
||||
# callbacks
|
||||
early_stop = EarlyStopping(
|
||||
monitor='val_acc',
|
||||
patience=3,
|
||||
mode='min',
|
||||
verbose=True,
|
||||
)
|
||||
|
||||
model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
|
||||
checkpoint = ModelCheckpoint(
|
||||
filepath=model_save_path,
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='val_acc',
|
||||
mode='min'
|
||||
)
|
||||
|
||||
# configure trainer
|
||||
trainer = Trainer(
|
||||
experiment=exp,
|
||||
checkpoint_callback=checkpoint,
|
||||
early_stop_callback=early_stop,
|
||||
)
|
||||
|
||||
# train model
|
||||
trainer.fit(model)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# use default args given by lightning
|
||||
root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
|
||||
parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
|
||||
add_default_args(parent_parser, root_dir)
|
||||
|
||||
# allow model to overwrite or extend args
|
||||
parser = ExampleModel.add_model_specific_args(parent_parser)
|
||||
hyperparams = parser.parse_args()
|
||||
|
||||
# train model
|
||||
main(hyperparams)
|
||||
Reference in New Issue
Block a user