293 lines
9.1 KiB
Python
293 lines
9.1 KiB
Python
from collections import OrderedDict
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from typing import Dict
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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 import optim
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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 DictHparamsModel(LightningModule):
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def __init__(self, hparams: Dict):
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super().__init__()
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self.hparams = hparams
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self.l1 = torch.nn.Linear(hparams.get('in_features'), hparams['out_features'])
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def forward(self, x):
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return torch.relu(self.l1(x.view(x.size(0), -1)))
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def training_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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return {'loss': F.cross_entropy(y_hat, y)}
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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(TrialMNIST(train=True, download=True), batch_size=16)
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class TestModelBase(LightningModule):
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"""Base LightningModule for testing. Implements only the required interface."""
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def __init__(self, hparams, force_remove_distributed_sampler: bool = False):
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"""Pass in parsed HyperOptArgumentParser to the model."""
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# init superclass
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super().__init__()
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self.hparams = hparams
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self.batch_size = hparams.batch_size
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# if you specify an example input, the summary will show input/output for each layer
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self.example_input_array = torch.rand(5, 28 * 28)
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# remove to test warning for dist sampler
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self.force_remove_distributed_sampler = force_remove_distributed_sampler
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# build model
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self.__build_model()
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# ---------------------
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# MODEL SETUP
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# ---------------------
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def __build_model(self):
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"""Layout model."""
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self.c_d1 = nn.Linear(in_features=self.hparams.in_features,
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out_features=self.hparams.hidden_dim)
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self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
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self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
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self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,
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out_features=self.hparams.out_features)
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# ---------------------
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# TRAINING
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# ---------------------
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def forward(self, x):
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"""No special modification required for lightning, define as you normally would."""
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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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logits = F.log_softmax(x, dim=1)
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return logits
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def loss(self, labels, logits):
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nll = F.nll_loss(logits, labels)
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return nll
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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"""Lightning calls this inside the training loop"""
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# forward pass
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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# calculate loss
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loss_val = self.loss(y, y_hat)
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# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
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if self.trainer.use_dp:
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loss_val = loss_val.unsqueeze(0)
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# alternate possible outputs to test
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if self.trainer.batch_idx % 1 == 0:
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output = OrderedDict({
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'loss': loss_val,
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'progress_bar': {'some_val': loss_val * loss_val},
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'log': {'train_some_val': loss_val * loss_val},
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})
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return output
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if self.trainer.batch_idx % 2 == 0:
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return loss_val
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# ---------------------
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# TRAINING SETUP
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# ---------------------
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def configure_optimizers(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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# try no scheduler for this model (testing purposes)
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if self.hparams.optimizer_name == 'lbfgs':
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optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate)
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else:
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)
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return [optimizer], [scheduler]
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def prepare_data(self):
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_ = TrialMNIST(root=self.hparams.data_root, train=True, download=True)
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def _dataloader(self, train):
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# init data generators
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dataset = TrialMNIST(root=self.hparams.data_root, train=train, download=True)
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# when using multi-node we need to add the datasampler
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batch_size = self.hparams.batch_size
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loader = DataLoader(
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dataset=dataset,
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batch_size=batch_size,
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shuffle=True
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)
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return loader
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class Generator(nn.Module):
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def __init__(self, latent_dim, img_shape):
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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):
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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, hparams):
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super().__init__()
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self.hparams = hparams
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# networks
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mnist_shape = (1, 28, 28)
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self.generator = Generator(latent_dim=hparams.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.hparams.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(
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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.hparams.learning_rate
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b1 = self.hparams.b1
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b2 = self.hparams.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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