157 lines
4.8 KiB
Python
157 lines
4.8 KiB
Python
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 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 Generator(nn.Module):
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def __init__(self, latent_dim: tuple, 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 TestGAN(LightningModule):
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"""Implements a basic GAN for the purpose of illustrating multiple optimizers."""
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def __init__(self, hidden_dim, learning_rate, b1, b2, **kwargs):
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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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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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