229 lines
7.0 KiB
Python
229 lines
7.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import OrderedDict
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import numpy as np
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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.utils.data import DataLoader
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from pytorch_lightning.core.lightning import LightningModule
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from tests.helpers.datasets import AverageDataset, MNIST, TrialMNIST
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class Generator(nn.Module):
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def __init__(self, latent_dim: int, img_shape: tuple):
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super().__init__()
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self.img_shape = img_shape
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def block(in_feat, out_feat, normalize=True):
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layers = [nn.Linear(in_feat, out_feat)]
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if normalize:
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layers.append(nn.BatchNorm1d(out_feat, 0.8))
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layers.append(nn.LeakyReLU(0.2, inplace=True))
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return layers
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self.model = nn.Sequential(
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*block(latent_dim, 128, normalize=False),
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*block(128, 256),
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*block(256, 512),
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*block(512, 1024),
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nn.Linear(1024, int(np.prod(img_shape))),
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nn.Tanh(),
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)
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def forward(self, z):
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img = self.model(z)
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img = img.view(img.size(0), *self.img_shape)
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return img
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class Discriminator(nn.Module):
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def __init__(self, img_shape: tuple):
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super().__init__()
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self.model = nn.Sequential(
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nn.Linear(int(np.prod(img_shape)), 512),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Linear(512, 256),
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nn.LeakyReLU(0.2, inplace=True),
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nn.Linear(256, 1),
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nn.Sigmoid(),
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)
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def forward(self, img):
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img_flat = img.view(img.size(0), -1)
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validity = self.model(img_flat)
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return validity
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class BasicGAN(LightningModule):
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"""Implements a basic GAN for the purpose of illustrating multiple optimizers."""
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def __init__(
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self, hidden_dim: int = 128, learning_rate: float = 0.001, b1: float = 0.5, b2: float = 0.999, **kwargs
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):
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super().__init__()
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self.hidden_dim = hidden_dim
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self.learning_rate = learning_rate
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self.b1 = b1
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self.b2 = b2
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# networks
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mnist_shape = (1, 28, 28)
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self.generator = Generator(latent_dim=self.hidden_dim, img_shape=mnist_shape)
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self.discriminator = Discriminator(img_shape=mnist_shape)
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# cache for generated images
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self.generated_imgs = None
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self.last_imgs = None
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self.example_input_array = torch.rand(2, self.hidden_dim)
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def forward(self, z):
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return self.generator(z)
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def adversarial_loss(self, y_hat, y):
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return F.binary_cross_entropy(y_hat, y)
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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imgs, _ = batch
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self.last_imgs = imgs
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# train generator
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if optimizer_idx == 0:
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# sample noise
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z = torch.randn(imgs.shape[0], self.hidden_dim)
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z = z.type_as(imgs)
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# generate images
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self.generated_imgs = self(z)
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# ground truth result (ie: all fake)
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# put on GPU because we created this tensor inside training_loop
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valid = torch.ones(imgs.size(0), 1)
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valid = valid.type_as(imgs)
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# adversarial loss is binary cross-entropy
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g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)
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tqdm_dict = {'g_loss': g_loss}
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output = OrderedDict({
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'loss': g_loss,
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'progress_bar': tqdm_dict,
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'log': tqdm_dict,
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})
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return output
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# train discriminator
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if optimizer_idx == 1:
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# Measure discriminator's ability to classify real from generated samples
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# how well can it label as real?
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valid = torch.ones(imgs.size(0), 1)
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valid = valid.type_as(imgs)
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real_loss = self.adversarial_loss(self.discriminator(imgs), valid)
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# how well can it label as fake?
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fake = torch.zeros(imgs.size(0), 1)
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fake = fake.type_as(fake)
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fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs.detach()), fake)
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# discriminator loss is the average of these
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d_loss = (real_loss + fake_loss) / 2
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tqdm_dict = {'d_loss': d_loss}
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output = OrderedDict({
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'loss': d_loss,
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'progress_bar': tqdm_dict,
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'log': tqdm_dict,
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})
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return output
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def configure_optimizers(self):
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lr = self.learning_rate
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b1 = self.b1
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b2 = self.b2
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opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
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opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
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return [opt_g, opt_d], []
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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 ParityModuleRNN(LightningModule):
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def __init__(self):
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super().__init__()
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self.rnn = nn.LSTM(10, 20, batch_first=True)
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self.linear_out = nn.Linear(in_features=20, out_features=5)
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self.example_input_array = torch.rand(2, 3, 10)
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def forward(self, x):
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seq, last = self.rnn(x)
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return self.linear_out(seq)
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def training_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self(x)
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loss = F.mse_loss(y_hat, y)
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return {'loss': loss}
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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(AverageDataset(), batch_size=30)
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class ParityModuleMNIST(LightningModule):
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def __init__(self):
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super().__init__()
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self.c_d1 = nn.Linear(in_features=28 * 28, out_features=128)
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self.c_d1_bn = nn.BatchNorm1d(128)
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self.c_d1_drop = nn.Dropout(0.3)
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self.c_d2 = nn.Linear(in_features=128, out_features=10)
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self.example_input_array = torch.rand(2, 1, 28, 28)
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def forward(self, x):
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x = x.view(x.size(0), -1)
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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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return x
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def training_step(self, batch, batch_nb):
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x, y = batch
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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return {'loss': loss}
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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(MNIST(
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train=True,
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download=True,
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), batch_size=128, num_workers=1)
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