from collections import OrderedDict import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.data import DataLoader from tests.base.datasets import TrialMNIST, AverageDataset, MNIST 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 Generator(nn.Module): def __init__(self, latent_dim: int, img_shape: tuple): 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: tuple): 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 BasicGAN(LightningModule): """Implements a basic GAN for the purpose of illustrating multiple optimizers.""" def __init__(self, hidden_dim: int = 128, learning_rate: float = 0.001, b1: float = 0.5, b2: float = 0.999, **kwargs): super().__init__() self.hidden_dim = hidden_dim self.learning_rate = learning_rate self.b1 = b1 self.b2 = b2 # networks mnist_shape = (1, 28, 28) self.generator = Generator(latent_dim=self.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 self.example_input_array = torch.rand(2, self.hidden_dim) 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.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.learning_rate b1 = self.b1 b2 = self.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) class ParityModuleRNN(LightningModule): def __init__(self): super().__init__() self.rnn = nn.LSTM(10, 20, batch_first=True) self.linear_out = nn.Linear(in_features=20, out_features=5) self.example_input_array = torch.rand(2, 3, 10) def forward(self, x): seq, last = self.rnn(x) return self.linear_out(seq) def training_step(self, batch, batch_nb): x, y = batch y_hat = self(x) loss = F.mse_loss(y_hat, y) return {'loss': loss} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) def train_dataloader(self): return DataLoader(AverageDataset(), batch_size=30) class ParityModuleMNIST(LightningModule): def __init__(self): super().__init__() self.c_d1 = nn.Linear(in_features=28 * 28, out_features=128) self.c_d1_bn = nn.BatchNorm1d(128) self.c_d1_drop = nn.Dropout(0.3) self.c_d2 = nn.Linear(in_features=128, out_features=10) self.example_input_array = torch.rand(2, 1, 28, 28) def forward(self, x): x = x.view(x.size(0), -1) 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) return x def training_step(self, batch, batch_nb): x, y = batch y_hat = self(x) loss = F.cross_entropy(y_hat, y) return {'loss': loss} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) def train_dataloader(self): return DataLoader(MNIST(train=True, download=True,), batch_size=128)