Conditional dcgan pytorch

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. When doing so, I condition my GAN on grayscale versions of the images.

conditional dcgan pytorch

Every so often, I want to compare the colorized, grayscale and ground truth version of the images. I use pytorch. Looking at the code I've included, these should give the same batches.

However, they don't. As Haran Rajkumar pointed in the comments, much better solution would involve concatenating both datasets beforehand and applying torch. DataLoader after that provided both torch. Dataset objects contain images in the exact same order at the beginning. Notice that one doesn't have to create separate class to perform this operation, torch.

Deep Learning 33: Conditional Generative Adversarial Network (C-GAN) : Coding in Google Colab

ConcatDataset provides this functionality out of the box. Not sure about your exact code, but this should be enough or at least enough to get you in the right direction :. Learn more. Asked 11 months ago. Active 11 months ago. Viewed times. You are loading the colored images and gray images in separate dataloaders, how do you ensure that they are paired correctly? Haran perhaps I phrased my question poorly. My question is how to pair the images correctly in the batches.

However, I can't get it right. Try using torch. Also have you checked what the first images the loader loads? Do the color and gray correspond to each other? In a different run, do the loaders load the same order of images? Doesn't change anything. The results are shown here imgur. The gray should just be the gray versions of the images, not completely different. I would recommend creating a new class that inherits dataset that loads both images sequentially.

This would be a more robust solution compared to playing with the seed values.Tensorflow implementation for Conditional Convolutional Adversarial Networks.

Conditioning of three-dimensional geological and pore scale generative adversarial networks. An enhanced zi2zi project with word-oriented data augmentation, feature combination, and transfer learning.

In terms of specific text description to generate corresponding images. Add a description, image, and links to the conditional-gan topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the conditional-gan topic, visit your repo's landing page and select "manage topics.

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All 59 Python 39 Jupyter Notebook 19 Julia 1. Sort options. Star 2k. Code Issues Pull requests. Collection of generative models in Pytorch version. Updated Dec 5, Python. Star Updated Sep 15, Jupyter Notebook. Updated Aug 22, Python. Updated Nov 22, Jupyter Notebook. Updated Oct 10, Python.

Updated Aug 10, Python. Updated May 29, Python.Click here to download the full example code. Author : Nathan Inkawhich. We will train a generative adversarial network GAN to generate new celebrities after showing it pictures of many real celebrities. Also, for the sake of time it will help to have a GPU, or two. Lets start from the beginning. They are made of two distinct models, a generator and a discriminator. The job of the discriminator is to look at an image and output whether or not it is a real training image or a fake image from the generator.

During training, the generator is constantly trying to outsmart the discriminator by generating better and better fakes, while the discriminator is working to become a better detective and correctly classify the real and fake images. Now, lets define some notation to be used throughout tutorial starting with the discriminator. From the paper, the GAN loss function is.

However, the convergence theory of GANs is still being actively researched and in reality models do not always train to this point. A DCGAN is a direct extension of the GAN described above, except that it explicitly uses convolutional and convolutional-transpose layers in the discriminator and generator, respectively.

It was first described by Radford et. The discriminator is made up of strided convolution layers, batch norm layers, and LeakyReLU activations.

conditional-gan

The input is a 3x64x64 input image and the output is a scalar probability that the input is from the real data distribution. The generator is comprised of convolutional-transpose layers, batch norm layers, and ReLU activations. The strided conv-transpose layers allow the latent vector to be transformed into a volume with the same shape as an image. In the paper, the authors also give some tips about how to setup the optimizers, how to calculate the loss functions, and how to initialize the model weights, all of which will be explained in the coming sections.

In this tutorial we will use the Celeb-A Faces dataset which can be downloaded at the linked site, or in Google Drive. Once downloaded, create a directory named celeba and extract the zip file into that directory. Then, set the dataroot input for this notebook to the celeba directory you just created. The resulting directory structure should be:. Now, we can create the dataset, create the dataloader, set the device to run on, and finally visualize some of the training data. With our input parameters set and the dataset prepared, we can now get into the implementation.

We will start with the weigth initialization strategy, then talk about the generator, discriminator, loss functions, and training loop in detail. This function is applied to the models immediately after initialization. In practice, this is accomplished through a series of strided two dimensional convolutional transpose layers, each paired with a 2d batch norm layer and a relu activation.

It is worth noting the existence of the batch norm functions after the conv-transpose layers, as this is a critical contribution of the DCGAN paper. These layers help with the flow of gradients during training. Notice, the how the inputs we set in the input section nzngfand nc influence the generator architecture in code.

InfoGAN: unsupervised conditional GAN in TensorFlow and Pytorch

Below is the code for the generator. Check out the printed model to see how the generator object is structured.Humans are very good at recognizing things and also creating new things.

For so long, we have worked on teaching computers to emulate human ability to recognize things but the ability to create new things eluded artificial intelligence systems for long.

In this post, we shall go through a basic overview of Generative Adversarial Networks and we shall use them to generate images of specific digits. Imagine you are an artist trying to draw a very realistic picture of Obama that will fool a judge into thinking the picture is a real picture. The first time you do this, the judge easily detects your picture is fake, then you try again and again until the judge is fooled into thinking the picture is real.

Generative Adversarial Networks works this way, it consists of two models. A Generator that draws images and a Discriminator that attempts to distinguish between real images and the images drawn by the discriminator. In a sense, both are competing with each other, the generator is trained to fool the discriminator while the discriminator is trained to properly tell apart which images are real and which are generated. In the end, the generator will become so perfect that the discriminator will not be able to tell apart between real and generated images.

Below are samples created by a GAN Generator. In this tutorial, we shall be using the conditional gans as they allow us to specify what we want to generate. Tranining GANs is usually complicated, but thanks to Torchfusion, a research framework built on PyTorch, the process will be super simple and very straightforward. Next, import a couple of classes. Define the generator network and the discriminator. In the above, we specify the resolution of the images to be generated as 1 x 32 x Setup the optimizers for both Generator and Discriminator models.

Now we need to load a dataset which we shall try to draw samples from. Below we create a Learner, torchfusion has various learners that are highly specialized for different purposes. And now, we can call the train function to train the two models. After just 20 epochs of training, this generates the image below:. Now to the most exciting part, using your trained model, you can easily generate new images of specific digits. In the code below, we generated a new image of digit 6, you can specify any digit between 0 — 9.

Generative Adversarial Networks are an exciting field of research, torchfusion makes it very simple with well optimized implementations of the best GAN algorithms. TorchFusion is developed and maintained by I and Moses Olafenwa, The AI Commons Team, as part of our efforts to democratize Artificial Intelligence and make it accessible to every single person and organization on the planet.

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. When doing so, I condition my GAN on grayscale versions of the images.

Every so often, I want to compare the colorized, grayscale and ground truth version of the images. I use pytorch. Looking at the code I've included, these should give the same batches.

However, they don't. As Haran Rajkumar pointed in the comments, much better solution would involve concatenating both datasets beforehand and applying torch. DataLoader after that provided both torch. Dataset objects contain images in the exact same order at the beginning.

Notice that one doesn't have to create separate class to perform this operation, torch. ConcatDataset provides this functionality out of the box. Not sure about your exact code, but this should be enough or at least enough to get you in the right direction :.

Learn more. Asked 11 months ago. Active 11 months ago. Viewed times.

InfoGAN: unsupervised conditional GAN in TensorFlow and Pytorch

You are loading the colored images and gray images in separate dataloaders, how do you ensure that they are paired correctly? Haran perhaps I phrased my question poorly. My question is how to pair the images correctly in the batches. However, I can't get it right.Click here to download the full example code.

At this point, we have seen various feed-forward networks. That is, there is no state maintained by the network at all. This might not be the behavior we want. Sequence models are central to NLP: they are models where there is some sort of dependence through time between your inputs. The classical example of a sequence model is the Hidden Markov Model for part-of-speech tagging. Another example is the conditional random field. A recurrent neural network is a network that maintains some kind of state.

For example, its output could be used as part of the next input, so that information can propogate along as the network passes over the sequence. We can use the hidden state to predict words in a language model, part-of-speech tags, and a myriad of other things.

Before getting to the example, note a few things. The semantics of the axes of these tensors is important. The first axis is the sequence itself, the second indexes instances in the mini-batch, and the third indexes elements of the input. In addition, you could go through the sequence one at a time, in which case the 1st axis will have size 1 also. In this section, we will use an LSTM to get part of speech tags.

We will not use Viterbi or Forward-Backward or anything like that, but as a challenging exercise to the reader, think about how Viterbi could be used after you have seen what is going on. To do the prediction, pass an LSTM over the sentence. That is, take the log softmax of the affine map of the hidden state, and the predicted tag is the tag that has the maximum value in this vector. In the example above, each word had an embedding, which served as the inputs to our sequence model.

We expect that this should help significantly, since character-level information like affixes have a large bearing on part-of-speech.

For example, words with the affix -ly are almost always tagged as adverbs in English. Total running time of the script: 0 minutes 1. Gallery generated by Sphinx-Gallery. To analyze traffic and optimize your experience, we serve cookies on this site.

By clicking or navigating, you agree to allow our usage of cookies. Learn more, including about available controls: Cookies Policy. Table of Contents. Run in Google Colab. Download Notebook. View on GitHub. Note Click here to download the full example code.

Author: Robert Guthrie import torch import torch. We will keep them small, so we can see how the weights change as we train. SGD model.The deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator.

Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.

conditional dcgan pytorch

The standard GAN uses a sigmoid cross entropy loss for the discriminator to classify whether its input is real or fake. However, if a generated sample is well classified as real by the discriminator, there would be no reason for the generator to be updated even though the generated sample is located far from the real data distribution.

PyTorchでDCGANやってみた

A sigmoid cross entropy loss can barely push such generated samples towards real data distribution since its classification role has been achieved. Motivated by this phenomenon, least-square GAN LSGAN replaces a sigmoid cross entropy loss with a least square loss, which directly penalizes fake samples by moving them close to the real data distribution. LSGAN solves the following problems:.

conditional dcgan pytorch

The above equation use a least square loss, under which the discriminator is forced to have designated values a, b and c for the real samples and the generated samples, respectively, rather than a probability for the real or fake samples. Thus, in contrary to a sigmoid cross entropy loss, a least square loss not only classifies the real samples and the generated samples but also pushes generated samples closer to the real data distribution.

In the original GAN, we have no control of what to be generated, since the output is only dependent on random noise. However, we can add a conditional input c to the random noise z so that the generated image is defined by G c, z. Typically, the conditional input vector c is concatenated with the noise vector z, and the resulting vector is put into the generator as it is in the original GAN. Besides, we can perform other data augmentation on c and z.

The meaning of conditional input c is arbitrary, for example, it can be the class of image, attributes of object or an embedding of text descriptions of the image we want to generate.

InfoGAN decomposes an input noise vector into a standard incompressible latent vector z and another latent variable c to capture salient semantic features of real samples. In CGAN, additional information c is assumed to be semantically known such as class labelsso we have to provide c to the generator and the discriminator during the training phase.

As a result, the automatically inferred c in InfoGAN has much more freedom to capture certain features of real data than c in CGAN, which is restricted to known information. In order to feed more side-information and to allow for semi-supervised learning, one can add an additional task-specific auxiliary classifier to the discriminator, so that the model is optimized on the original tasks as well as the additional task.

The architecture of such method is illustrated in the below figure, where C is the auxiliary classifier. Adding auxiliary classifiers allows us to use pre-trained models e. Using auxiliary classifiers can also help in applications such as text-to-image synthesis and image-to-image translation. It focuses on matching loss distributions through Wasserstein distance and not on directly matching data distributions.

In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images. It can learn a joint distribution with just samples drawn from the marginal distributions. This is achieved by enforcing a weight-sharing constraint that limits the network capacity and favors a joint distribution solution over a product of marginal distributions one.

We find that these problems are often due to the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, which can lead to undesired behavior. We propose an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input. Sequential nn. Linear opt. BatchNorm2dnn. BatchNorm2d0.

LeakyReLU 0. BatchNorm2d 640. Conv2d 64opt. Dropout2d 0. Embedding opt. Linearint np. Linear, nn.

conditional dcgan pytorch