Package: pytorch-pretrained-biggan
PyTorch pretrained BigGANAn op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind.IntroductionThis repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paperLarge Scale GAN Training for High Fidelity Natural Image Synthesisby Andrew Brocky, Jeff Donahuey and Karen Simonyan.This PyTorch implementation of BigGAN is provided with thepretrained 128x128, 256x256 and 512x512 models by DeepMind. We also provide the scripts used to download and convert these models from the TensorFlow Hub models.This reimplementation was done from the raw computation graph of the Tensorflow version and behave similarly to the TensorFlow version (variance of the output difference of the order of 1e5).InstallationThis repo was tested on Python 3.6 and PyTorch 1.0.1PyTorch pretrained BigGAN can be installed by pip as follows:pipinstallpytorch-pretrained-bigganIf you simply want to play with the GAN this should be enough.If you want to use the conversion scripts and the imagenet utilities, additional requirements are needed, in particular TensorFlow and NLTK. To install all the requirements please use thefull_requirements.txtfile:gitclonehttps://github.com/huggingface/pytorch-pretrained-BigGAN.gitcdpytorch-pretrained-BigGAN
pipinstall-rfull_requirements.txtModelsThis repository provide direct and simple access to the pretrained "deep" versions of BigGAN for 128, 256 and 512 pixels resolutions as described in theassociated publication.
Here are some details on the models:BigGAN-deep-128: a 50.4M parameters model generating 128x128 pixels images, the model dump weights 201 MB,BigGAN-deep-256: a 55.9M parameters model generating 256x256 pixels images, the model dump weights 224 MB,BigGAN-deep-512: a 56.2M parameters model generating 512x512 pixels images, the model dump weights 225 MB.Please refer to Appendix B of the paper for details on the architectures.All models comprise pre-computed batch norm statistics for 51 truncation values between 0 and 1 (see Appendix C.1 in the paper for details).UsageHere is a quick-start example usingBigGANwith a pre-trained model.See thedoc sectionbelow for details on these classes and methods.importtorchfrompytorch_pretrained_bigganimport(BigGAN,one_hot_from_names,truncated_noise_sample,save_as_images,display_in_terminal)# OPTIONAL: if you want to have more information on what's happening, activate the logger as followsimportlogginglogging.basicConfig(level=logging.INFO)# Load pre-trained model tokenizer (vocabulary)model=BigGAN.from_pretrained('biggan-deep-256')# Prepare a inputtruncation=0.4class_vector=one_hot_from_names(['soap bubble','coffee','mushroom'],batch_size=3)noise_vector=truncated_noise_sample(truncation=truncation,batch_size=3)# All in tensorsnoise_vector=torch.from_numpy(noise_vector)class_vector=torch.from_numpy(class_vector)# Generate an imageoutput=model(noise_vector,class_vector,truncation)# If you have a sixtel compatible terminal you can display the images in the terminal# (see https://github.com/saitoha/libsixel for details)display_in_terminal(output)# Save results as png imagessave_as_images(output)DocLoading DeepMind's pre-trained weightsTo load one of DeepMind's pre-trained models, instantiate aBigGANmodel withfrom_pretrained()as:model=BigGAN.from_pretrained(PRE_TRAINED_MODEL_NAME_OR_PATH,cache_dir=None)wherePRE_TRAINED_MODEL_NAME_OR_PATHis either:the shortcut name of a Google AI's or OpenAI's pre-trained model selected in the list:biggan-deep-128: 12-layer, 768-hidden, 12-heads, 110M parametersbiggan-deep-256: 24-layer, 1024-hidden, 16-heads, 340M parametersbiggan-deep-512: 12-layer, 768-hidden, 12-heads , 110M parametersa path or url to a pretrained model archive containing:config.json: a configuration file for the model, andpytorch_model.bina PyTorch dump of a pre-trained instance ofBigGAN(saved with the usualtorch.save()).IfPRE_TRAINED_MODEL_NAME_OR_PATHis a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the linkshere) and stored in a cache folder to avoid future download (the cache folder can be found at~/.pytorch_pretrained_biggan/).cache_dircan be an optional path to a specific directory to download and cache the pre-trained model weights.ConfigurationBigGANConfigis a class to store and load BigGAN configurations. It's defined inconfig.py.Here are some details on the attributes:output_dim: output resolution of the GAN (128, 256 or 512) for the pre-trained models,z_dim: size of the noise vector (128 for the pre-trained models).class_embed_dim: size of the class embedding vectors (128 for the pre-trained models).channel_width: size of each channel (128 for the pre-trained models).num_classes: number of classes in the training dataset, like imagenet (1000 for the pre-trained models).layers: A list of layers definition. Each definition for a layer is a triple of [up-sample in the layer ? (bool), number of input channels (int), number of output channels (int)]attention_layer_position: Position of the self-attention layer in the layer hierarchy (8 for the pre-trained models).eps: epsilon value to use for spectral and batch normalization layers (1e-4 for the pre-trained models).n_stats: number of pre-computed statistics for the batch normalization layers associated to various truncation values between 0 and 1 (51 for the pre-trained models).ModelBigGANis a PyTorch model (torch.nn.Module) of BigGAN defined inmodel.py. This model comprises the class embeddings (a linear layer) and the generator with a series of convolutions and conditional batch norms. The discriminator is currently not implemented since pre-trained weights have not been released for it.The inputs and output areidentical to the TensorFlow model inputs and outputs.We detail them here.BigGANtakes asinputs:z: a torch.FloatTensor of shape [batch_size, config.z_dim] with noise sampled from a truncated normal distribution, andclass_label: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token types indices selected in [0, 1]. Type 0 corresponds to asentence Aand type 1 corresponds to asentence Btoken (see BERT paper for more details).truncation: a float between 0 (not comprised) and 1. The truncation of the truncated normal used for creating the noise vector. This truncation value is used to selecte between a set of pre-computed statistics (means and variances) for the batch norm layers.BigGANoutputsan array of shape [batch_size, 3, resolution, resolution] where resolution is 128, 256 or 512 depending of the model:Utilities: Images, Noise, Imagenet classesWe provide a few utility method to use the model. They are defined inutils.py.Here are some details on these methods:truncated_noise_sample(batch_size=1, dim_z=128, truncation=1., seed=None):Create a truncated noise vector.Params:batch_size: batch size.dim_z: dimension of ztruncation: truncation value to useseed: seed for the random generatorOutput:
array of shape (batch_size, dim_z)convert_to_images(obj):Convert an output tensor from BigGAN in a list of images.Params:obj: tensor or numpy array of shape (batch_size, channels, height, width)Output:list of Pillow Images of size (height, width)save_as_images(obj, file_name='output'):Convert and save an output tensor from BigGAN in a list of saved images.Params:obj: tensor or numpy array of shape (batch_size, channels, height, width)file_name: path and beggingin of filename to save.
Images will be saved asfile_name_{image_number}.pngdisplay_in_terminal(obj):Convert and display an output tensor from BigGAN in the terminal. This function uselibsixeland will only work in a libsixel-compatible terminal. Please refer tohttps://github.com/saitoha/libsixelfor more details.Params:obj: tensor or numpy array of shape (batch_size, channels, height, width)file_name: path and beggingin of filename to save.
Images will be saved asfile_name_{image_number}.pngone_hot_from_int(int_or_list, batch_size=1):Create a one-hot vector from a class index or a list of class indices.Params:int_or_list: int, or list of int, of the imagenet classes (between 0 and 999)batch_size: batch size.If int_or_list is an int create a batch of identical classes.If int_or_list is a list, we should havelen(int_or_list) == batch_sizeOutput:array of shape (batch_size, 1000)one_hot_from_names(class_name, batch_size=1):Create a one-hot vector from the name of an imagenet class ('tennis ball', 'daisy', ...). We use NLTK's wordnet search to try to find the relevant synset of ImageNet and take the first one. If we can't find it direcly, we look at the hyponyms and hypernyms of the class name.Params:class_name: string containing the name of an imagenet object.Output:array of shape (batch_size, 1000)Download and conversion scriptsScripts to download and convert the TensorFlow models from TensorFlow Hub are provided in./scripts.The scripts can be used directly as:./scripts/download_tf_hub_models.sh
./scripts/convert_tf_hub_models.sh