242 lines
7.4 KiB
Python
242 lines
7.4 KiB
Python
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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return loss_val
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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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return loss_val
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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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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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