221 lines
7.2 KiB
Python
221 lines
7.2 KiB
Python
import os
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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 torchvision import transforms
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from torchvision.datasets import MNIST
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try:
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from test_tube import HyperOptArgumentParser
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except ImportError:
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# TODO: this should be discussed and moved out of this package
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raise ImportError('Missing test-tube package.')
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from pytorch_lightning.core.decorators import data_loader
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from pytorch_lightning.core.lightning import LightningModule
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class TestingMNIST(MNIST):
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def __init__(self, root, train=True, transform=None, target_transform=None,
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download=False, num_samples=8000):
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super(TestingMNIST, self).__init__(
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root,
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train=train,
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transform=transform,
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target_transform=target_transform,
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download=download
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)
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# take just a subset of MNIST dataset
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self.data = self.data[:num_samples]
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self.targets = self.targets[:num_samples]
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class LightningTestModelBase(LightningModule):
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"""
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Base LightningModule for testing. Implements only the required
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interface
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"""
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def __init__(self, hparams, force_remove_distributed_sampler=False):
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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(LightningTestModelBase, self).__init__()
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self.hparams = hparams
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self.batch_size = hparams.batch_size
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# if you specify an example input, the summary will show input/output for each layer
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self.example_input_array = torch.rand(5, 28 * 28)
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# remove to test warning for dist sampler
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self.force_remove_distributed_sampler = force_remove_distributed_sampler
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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,
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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,
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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, batch, batch_idx):
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"""
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Lightning calls this inside the training loop
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:param batch:
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:return:
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"""
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# forward pass
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x, y = 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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# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
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if self.trainer.use_dp:
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loss_val = loss_val.unsqueeze(0)
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# alternate possible outputs to test
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if self.trainer.batch_idx % 1 == 0:
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output = OrderedDict({
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'loss': loss_val,
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'progress_bar': {'some_val': loss_val * loss_val},
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'log': {'train_some_val': loss_val * loss_val},
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})
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return output
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if self.trainer.batch_idx % 2 == 0:
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return loss_val
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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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# try no scheduler for this model (testing purposes)
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if self.hparams.optimizer_name == 'lbfgs':
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optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate)
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else:
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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# test returning only 1 list instead of 2
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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(),
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transforms.Normalize((0.5,), (1.0,))])
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dataset = TestingMNIST(root=self.hparams.data_root, train=train,
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transform=transform, download=True, num_samples=2000)
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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.use_ddp and not self.force_remove_distributed_sampler:
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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:
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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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@data_loader
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def train_dataloader(self):
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return self._dataloader(train=True)
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@staticmethod
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def add_model_specific_args(parent_parser, root_dir): # pragma: no cover
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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_val=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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# use 500 for CPU, 50000 for GPU to see speed difference
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parser.add_argument('--hidden_dim', default=50000, type=int)
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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,
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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,
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options=['adam'], tunable=False)
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# if using 2 nodes with 4 gpus each the batch size here
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# (256) will be 256 / (2*8) = 16 per gpu
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parser.opt_list('--batch_size', default=256 * 8, type=int,
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options=[32, 64, 128, 256], tunable=False,
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help='batch size will be divided over all gpus being used across all nodes')
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return parser
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