258 lines
7.9 KiB
Python
258 lines
7.9 KiB
Python
"""
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Example template for defining a system
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"""
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import logging as log
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import os
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from argparse import ArgumentParser
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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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import torchvision.transforms as transforms
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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.datasets import MNIST
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import pytorch_lightning as pl
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class LightningTemplateModel(pl.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__()
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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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# 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 or self.trainer.use_ddp2:
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loss_val = loss_val.unsqueeze(0)
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tqdm_dict = {'train_loss': loss_val}
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output = OrderedDict({
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'loss': loss_val,
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'progress_bar': tqdm_dict,
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'log': tqdm_dict
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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, batch, batch_idx):
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"""
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Lightning calls this inside the validation loop
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:param batch:
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:return:
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"""
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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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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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val_acc = torch.tensor(val_acc)
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if self.on_gpu:
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val_acc = val_acc.cuda(loss_val.device.index)
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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 or self.trainer.use_ddp2:
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loss_val = loss_val.unsqueeze(0)
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val_acc = val_acc.unsqueeze(0)
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output = OrderedDict({
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'val_loss': loss_val,
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'val_acc': val_acc,
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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 = output['val_loss']
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# reduce manually when using dp
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if self.trainer.use_dp or self.trainer.use_ddp2:
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val_loss = torch.mean(val_loss)
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val_loss_mean += val_loss
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# reduce manually when using dp
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val_acc = output['val_acc']
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if self.trainer.use_dp or self.trainer.use_ddp2:
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val_acc = torch.mean(val_acc)
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val_acc_mean += 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_dict = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}
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result = {'progress_bar': tqdm_dict, 'log': tqdm_dict, 'val_loss': val_loss_mean}
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return result
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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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scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
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return [optimizer], [scheduler]
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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 = MNIST(root=self.hparams.data_root, train=train,
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transform=transform, download=True)
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# when using multi-node (ddp) 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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if self.use_ddp:
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train_sampler = DistributedSampler(dataset)
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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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num_workers=0
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)
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return loader
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@pl.data_loader
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def train_dataloader(self):
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log.info('Training data loader called.')
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return self.__dataloader(train=True)
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@pl.data_loader
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def val_dataloader(self):
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log.info('Validation data loader called.')
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return self.__dataloader(train=False)
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@pl.data_loader
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def test_dataloader(self):
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log.info('Test data loader called.')
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return self.__dataloader(train=False)
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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 = ArgumentParser(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.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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parser.add_argument('--drop_prob', default=0.2, type=float)
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parser.add_argument('--learning_rate', default=0.001, type=float)
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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.add_argument('--optimizer_name', default='adam', type=str)
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parser.add_argument('--batch_size', default=64, type=int)
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return parser
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