from collections import OrderedDict from typing import Dict import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch import optim from torch.utils.data import DataLoader from tests.base.datasets import TrialMNIST try: from test_tube import HyperOptArgumentParser except ImportError: # TODO: this should be discussed and moved out of this package raise ImportError('Missing test-tube package.') from pytorch_lightning.core.lightning import LightningModule class DictHparamsModel(LightningModule): def __init__(self, hparams: Dict): super().__init__() self.hparams = hparams self.l1 = torch.nn.Linear(hparams.get('in_features'), hparams['out_features']) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_idx): x, y = batch y_hat = self(x) return {'loss': F.cross_entropy(y_hat, y)} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) def train_dataloader(self): return DataLoader(TrialMNIST(train=True, download=True), batch_size=16) class TestModelBase(LightningModule): """Base LightningModule for testing. Implements only the required interface.""" def __init__(self, hparams, force_remove_distributed_sampler: bool = False): """Pass in parsed HyperOptArgumentParser to the model.""" # init superclass super().__init__() self.hparams = hparams self.batch_size = hparams.batch_size # if you specify an example input, the summary will show input/output for each layer self.example_input_array = torch.rand(5, 28 * 28) # remove to test warning for dist sampler self.force_remove_distributed_sampler = force_remove_distributed_sampler # build model self.__build_model() # --------------------- # MODEL SETUP # --------------------- def __build_model(self): """Layout model.""" self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim) self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim) self.c_d1_drop = nn.Dropout(self.hparams.drop_prob) self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features) # --------------------- # TRAINING # --------------------- def forward(self, x): """No special modification required for lightning, define as you normally would.""" x = self.c_d1(x) x = torch.tanh(x) x = self.c_d1_bn(x) x = self.c_d1_drop(x) x = self.c_d2(x) logits = F.log_softmax(x, dim=1) return logits def loss(self, labels, logits): nll = F.nll_loss(logits, labels) return nll def training_step(self, batch, batch_idx, optimizer_idx=None): """Lightning calls this inside the training loop""" # forward pass x, y = batch x = x.view(x.size(0), -1) y_hat = self(x) # calculate loss loss_val = self.loss(y, y_hat) # in DP mode (default) make sure if result is scalar, there's another dim in the beginning if self.trainer.use_dp: loss_val = loss_val.unsqueeze(0) # alternate possible outputs to test if self.trainer.batch_idx % 1 == 0: output = OrderedDict({ 'loss': loss_val, 'progress_bar': {'some_val': loss_val * loss_val}, 'log': {'train_some_val': loss_val * loss_val}, }) return output if self.trainer.batch_idx % 2 == 0: return loss_val # --------------------- # TRAINING SETUP # --------------------- def configure_optimizers(self): """ return whatever optimizers we want here. :return: list of optimizers """ # try no scheduler for this model (testing purposes) if self.hparams.optimizer_name == 'lbfgs': optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate) else: optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate) scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10) return [optimizer], [scheduler] def prepare_data(self): _ = TrialMNIST(root=self.hparams.data_root, train=True, download=True) def _dataloader(self, train): # init data generators dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True) # when using multi-node we need to add the datasampler batch_size = self.hparams.batch_size loader = DataLoader( dataset=dataset, batch_size=batch_size, shuffle=True ) return loader class Generator(nn.Module): def __init__(self, latent_dim, img_shape): super().__init__() self.img_shape = img_shape def block(in_feat, out_feat, normalize=True): layers = [nn.Linear(in_feat, out_feat)] if normalize: layers.append(nn.BatchNorm1d(out_feat, 0.8)) layers.append(nn.LeakyReLU(0.2, inplace=True)) return layers self.model = nn.Sequential( *block(latent_dim, 128, normalize=False), *block(128, 256), *block(256, 512), *block(512, 1024), nn.Linear(1024, int(np.prod(img_shape))), nn.Tanh() ) def forward(self, z): img = self.model(z) img = img.view(img.size(0), *self.img_shape) return img class Discriminator(nn.Module): def __init__(self, img_shape): super().__init__() self.model = nn.Sequential( nn.Linear(int(np.prod(img_shape)), 512), nn.LeakyReLU(0.2, inplace=True), nn.Linear(512, 256), nn.LeakyReLU(0.2, inplace=True), nn.Linear(256, 1), nn.Sigmoid(), ) def forward(self, img): img_flat = img.view(img.size(0), -1) validity = self.model(img_flat) return validity class TestGAN(LightningModule): """Implements a basic GAN for the purpose of illustrating multiple optimizers.""" def __init__(self, hparams): super().__init__() self.hparams = hparams # networks mnist_shape = (1, 28, 28) self.generator = Generator(latent_dim=hparams.hidden_dim, img_shape=mnist_shape) self.discriminator = Discriminator(img_shape=mnist_shape) # cache for generated images self.generated_imgs = None self.last_imgs = None def forward(self, z): return self.generator(z) def adversarial_loss(self, y_hat, y): return F.binary_cross_entropy(y_hat, y) def training_step(self, batch, batch_idx, optimizer_idx=None): imgs, _ = batch self.last_imgs = imgs # train generator if optimizer_idx == 0: # sample noise z = torch.randn(imgs.shape[0], self.hparams.hidden_dim) z = z.type_as(imgs) # generate images self.generated_imgs = self(z) # ground truth result (ie: all fake) # put on GPU because we created this tensor inside training_loop valid = torch.ones(imgs.size(0), 1) valid = valid.type_as(imgs) # adversarial loss is binary cross-entropy g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid) tqdm_dict = {'g_loss': g_loss} output = OrderedDict({ 'loss': g_loss, 'progress_bar': tqdm_dict, 'log': tqdm_dict }) return output # train discriminator if optimizer_idx == 1: # Measure discriminator's ability to classify real from generated samples # how well can it label as real? valid = torch.ones(imgs.size(0), 1) valid = valid.type_as(imgs) real_loss = self.adversarial_loss(self.discriminator(imgs), valid) # how well can it label as fake? fake = torch.zeros(imgs.size(0), 1) fake = fake.type_as(fake) fake_loss = self.adversarial_loss( self.discriminator(self.generated_imgs.detach()), fake) # discriminator loss is the average of these d_loss = (real_loss + fake_loss) / 2 tqdm_dict = {'d_loss': d_loss} output = OrderedDict({ 'loss': d_loss, 'progress_bar': tqdm_dict, 'log': tqdm_dict }) return output def configure_optimizers(self): lr = self.hparams.learning_rate b1 = self.hparams.b1 b2 = self.hparams.b2 opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2)) opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2)) return [opt_g, opt_d], [] def train_dataloader(self): return DataLoader(TrialMNIST(train=True, download=True), batch_size=16)