# Copyright The Lightning AI team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from lightning.pytorch.core.module import LightningModule from torch.utils.data import DataLoader from tests_pytorch import _PATH_DATASETS from tests_pytorch.helpers.datasets import MNIST, AverageDataset, TrialMNIST 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) return self.model(img_flat) 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.automatic_optimization = False 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): imgs, _ = batch self.last_imgs = imgs optimizer1, optimizer2 = self.optimizers() # train generator # sample noise self.toggle_optimizer(optimizer1) 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) self.log("g_loss", g_loss, prog_bar=True, logger=True) self.manual_backward(g_loss) optimizer1.step() optimizer1.zero_grad() self.untoggle_optimizer(optimizer1) # train discriminator # Measure discriminator's ability to classify real from generated samples self.toggle_optimizer(optimizer2) # 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 self.log("d_loss", d_loss, prog_bar=True, logger=True) self.manual_backward(d_loss) optimizer2.step() optimizer2.zero_grad() self.untoggle_optimizer(optimizer2) 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(root=_PATH_DATASETS, 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) self._loss = [] # needed for checking if the loss is the same as vanilla torch def forward(self, x): seq, _ = 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) self._loss.append(loss.item()) 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) self._loss = [] # needed for checking if the loss is the same as vanilla torch 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) self._loss.append(loss.item()) return {"loss": loss} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) def train_dataloader(self): return DataLoader(MNIST(root=_PATH_DATASETS, train=True, download=True), batch_size=128, num_workers=1)