156 lines
4.8 KiB
Python
156 lines
4.8 KiB
Python
import os
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from collections import OrderedDict
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from typing import Dict
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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 tests.base.datasets import TrialMNIST
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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.lightning import LightningModule
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class DictHparamsModel(LightningModule):
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def __init__(self, hparams: Dict):
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super().__init__()
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self.hparams = hparams
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self.l1 = torch.nn.Linear(hparams.get('in_features'), hparams['out_features'])
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def forward(self, x):
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return torch.relu(self.l1(x.view(x.size(0), -1)))
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def training_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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return {'loss': F.cross_entropy(y_hat, y)}
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=0.02)
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def train_dataloader(self):
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return DataLoader(TrialMNIST(train=True, download=True), batch_size=16)
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class TestModelBase(LightningModule):
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"""Base LightningModule for testing. Implements only the required interface."""
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def __init__(self, hparams, force_remove_distributed_sampler: bool = False):
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"""Pass in parsed HyperOptArgumentParser to the model."""
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# init superclass
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super().__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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"""Layout model."""
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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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"""No special modification required for lightning, define as you normally would."""
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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, optimizer_idx=None):
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"""Lightning calls this inside the training loop"""
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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(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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scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
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return [optimizer], [scheduler]
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def prepare_data(self):
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_ = TrialMNIST(root=self.hparams.data_root, train=True, download=True)
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def _dataloader(self, train):
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# init data generators
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dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True)
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# when using multi-node we need to add the datasampler
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batch_size = self.hparams.batch_size
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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=True
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)
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return loader
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