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robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/radam.py | """
---
title: Rectified Adam (RAdam) optimizer
summary: A simple PyTorch implementation/tutorial of RAdam optimizer.
---
# Rectified Adam (RAdam) optimizer
This implementation is based on
[the official implementation](https://github.com/LiyuanLucasLiu/RAdam)
of the paper
[On the Variance of the Adaptive Learning Rat... | 11,243 | 38.591549 | 134 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/adam_warmup_cosine_decay.py | """
---
title: Adam optimizer with warm-up and cosine decay
summary: A PyTorch implementation/tutorial of Adam optimizer with warm-up and cosine decay for GPT.
---
# Adam Optimizer with Warmup and Cosine Decay
This extends [AMSGrad optimizer](adam.html) and adds a warmup stage.
"""
import math
from typing import Dict... | 3,679 | 36.55102 | 121 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/mnist_experiment.py | """
---
title: MNIST example to test the optimizers
summary: This is a simple MNIST example with a CNN model to test the optimizers.
---
# MNIST example to test the optimizers
"""
import torch.nn as nn
import torch.utils.data
from labml_helpers.module import Module
from labml import experiment, tracker
from labml.con... | 4,063 | 28.449275 | 87 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/adam.py | """
---
title: Adam Optimizer
summary: A simple PyTorch implementation/tutorial of Adam optimizer
---
# Adam Optimizer
This is a [PyTorch](https://pytorch.org) implementation of popular optimizer *Adam* from paper
[Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980v9).
*Adam* update is,
\b... | 8,609 | 39.046512 | 118 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/performance_test.py | """
---
title: Test performance of Adam implementations
summary: This experiment compares performance of Adam implementations.
---
# Performance testing Adam
```
TorchAdam warmup...[DONE] 222.59ms
TorchAdam...[DONE] 1,356.01ms
MyAdam warmup...[DONE] 119.15ms
MyAdam...[DONE] 1,192.89ms
```
[![Open In Colab](https://c... | 1,608 | 27.732143 | 163 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/__init__.py | """
---
title: Optimizers
summary: >
A set of PyTorch implementations/tutorials of popular gradient descent based optimizers.
Currently includes Adam, AMSGrad and RAdam optimizers.
---
# Optimizers
## Optimizer Implementations
* [Adam Optimizer](adam.html)
* [AMSGrad Optimizer](amsgrad.html)
* [Adam Optimizer with ... | 8,108 | 36.892523 | 112 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/optimizers/configs.py | """
---
title: Configurable optimizer module
summary: This implements a configurable module for optimizers.
---
# Configurable Optimizer
"""
from typing import Tuple
import torch
from labml.configs import BaseConfigs, option, meta_config
from labml_nn.optimizers import WeightDecay
class OptimizerConfigs(BaseConfi... | 4,805 | 31.693878 | 90 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/utils/tokenizer.py | from typing import Callable
from labml.configs import BaseConfigs, option
class TokenizerConfigs(BaseConfigs):
"""
<a id="OptimizerConfigs">
## Optimizer Configurations
</a>
"""
tokenizer: Callable = 'character'
def __init__(self):
super().__init__(_primary='tokenizer')
@optio... | 970 | 18.039216 | 66 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/utils/__init__.py | """
---
title: Utilities
summary: A bunch of utility functions and classes
---
# Utilities
"""
import copy
from torch.utils.data import Dataset, IterableDataset
from labml_helpers.module import M, TypedModuleList
def clone_module_list(module: M, n: int) -> TypedModuleList[M]:
"""
## Clone Module
Make... | 1,548 | 22.830769 | 116 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/rl/ppo/experiment.py | """
---
title: PPO Experiment with Atari Breakout
summary: Annotated implementation to train a PPO agent on Atari Breakout game.
---
# PPO Experiment with Atari Breakout
This experiment trains Proximal Policy Optimization (PPO) agent Atari Breakout game on OpenAI Gym.
It runs the [game environments on multiple proce... | 15,265 | 35.96368 | 173 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/rl/ppo/__init__.py | """
---
title: Proximal Policy Optimization - PPO
summary: >
An annotated implementation of Proximal Policy Optimization - PPO algorithm in PyTorch.
---
# Proximal Policy Optimization - PPO
This is a [PyTorch](https://pytorch.org) implementation of
[Proximal Policy Optimization - PPO](https://arxiv.org/abs/1707.0634... | 7,536 | 35.410628 | 173 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/rl/ppo/gae.py | """
---
title: Generalized Advantage Estimation (GAE)
summary: A PyTorch implementation/tutorial of Generalized Advantage Estimation (GAE).
---
# Generalized Advantage Estimation (GAE)
This is a [PyTorch](https://pytorch.org) implementation of paper
[Generalized Advantage Estimation](https://arxiv.org/abs/1506.02438)... | 3,032 | 33.862069 | 96 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/rl/dqn/experiment.py | """
---
title: DQN Experiment with Atari Breakout
summary: Implementation of DQN experiment with Atari Breakout
---
# DQN Experiment with Atari Breakout
This experiment trains a Deep Q Network (DQN) to play Atari Breakout game on OpenAI Gym.
It runs the [game environments on multiple processes](../game.html) to sampl... | 9,352 | 35.968379 | 109 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/rl/dqn/model.py | """
---
title: Deep Q Network (DQN) Model
summary: Implementation of neural network model for Deep Q Network (DQN).
---
# Deep Q Network (DQN) Model
"""
import torch
from torch import nn
from labml_helpers.module import Module
class Model(Module):
"""
## Dueling Network ⚔️ Model for $Q$ Values
We are ... | 3,277 | 30.219048 | 106 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/rl/dqn/__init__.py | """
---
title: Deep Q Networks (DQN)
summary: >
This is a PyTorch implementation/tutorial of Deep Q Networks (DQN) from paper
Playing Atari with Deep Reinforcement Learning.
This includes dueling network architecture, a prioritized replay buffer and
double-Q-network training.
---
# Deep Q Networks (DQN)
This... | 6,291 | 36.452381 | 115 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/label_smoothing_loss.py | """
---
title: Label Smoothing Loss
summary: >
This is an implementation of label smoothing loss, that can be used as
an alternative to cross entropy loss for improved accuracy.
---
# Label Smoothing Loss
"""
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from labml_helpers.... | 2,210 | 30.585714 | 76 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/utils.py | """
---
title: Utilities for Transformer
summary: A bunch of utility functions and classes for transformers.
---
# Utilities for Transformer
"""
import torch
def subsequent_mask(seq_len):
"""
## Subsequent mask to mask out data from future (subsequent) time steps
"""
mask = torch.tril(torch.ones(seq... | 538 | 18.25 | 80 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/feed_forward.py | """
---
title: Position-wise Feed-Forward Network (FFN)
summary: Documented reusable implementation of the position wise feedforward network.
---
# Position-wise Feed-Forward Network (FFN)
This is a [PyTorch](https://pytorch.org) implementation
of position-wise feedforward network used in transformer.
FFN consists ... | 3,582 | 36.715789 | 107 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/mha.py | """
---
title: Multi-Headed Attention (MHA)
summary: >
This implements the Multi-Headed Attention used in transformers
using PyTorch with explanations.
---
# Multi-Headed Attention (MHA)
This is a tutorial/implementation of multi-headed attention
from paper [Attention Is All You Need](https://arxiv.org/abs/1706.0... | 6,881 | 34.112245 | 116 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/positional_encoding.py | """
---
title: Fixed Positional Encodings
summary: >
Implementation with explanation of fixed positional encodings as
described in paper Attention is All You Need.
---
# Fixed Positional Encodings
The positional encoding encodes the position along the sequence into
a vector of size `d_model`.
\begin{align}
PE_{... | 2,327 | 28.468354 | 102 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/models.py | """
---
title: Transformer Encoder and Decoder Models
summary: >
These are PyTorch implementations of Transformer based encoder and decoder models,
as well as other related modules.
---
# Transformer Encoder and Decoder Models
"""
import math
import torch
import torch.nn as nn
from labml_helpers.module import Mod... | 7,999 | 33.334764 | 116 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/__init__.py | """
---
title: Transformers
summary: >
This is a collection of PyTorch implementations/tutorials of
transformers and related techniques.
---
# Transformers
This module contains [PyTorch](https://pytorch.org/)
implementations and explanations of original transformer
from paper [Attention Is All You Need](https://a... | 3,550 | 35.989583 | 129 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/configs.py | """
---
title: Configurable Transformer Components
summary: These are configurable components that can be re-used quite easily.
---
# Configurable Transformer Components
"""
import copy
import torch.nn as nn
from labml.configs import BaseConfigs, option, calculate, aggregate
from labml_helpers.module import Module
f... | 10,324 | 30.574924 | 114 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/aft/experiment.py | """
---
title: Attention Free Transformer (AFT) Experiment
summary: This experiment trains an Attention Free Transformer (AFT) based model on Tiny Shakespeare dataset.
---
# [Attention Free Transformer (AFT)](index.html) Experiment
This is an annotated PyTorch experiment to train a [AFT model](index.html).
This is b... | 4,988 | 29.054217 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/aft/__init__.py | """
---
title: An Attention Free Transformer
summary: >
This is an annotated implementation/tutorial of the AFT (Attention Free Transformer) in PyTorch.
---
# An Attention Free Transformer
This is a [PyTorch](https://pytorch.org) implementation of the paper
[An Attention Free Transformer](https://papers.labml.ai/pa... | 8,600 | 35.6 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/fast_weights/experiment.py | """
---
title: Train Fast Weights Transformer
summary: This is training code with notes for a Fast Weights Transformer.
---
# Train Fast Weights Transformer
This trains a fast weights transformer model for auto-regression.
Here’s a Colab notebook for training a fast weights transformer on Tiny Shakespeare dataset.
... | 3,830 | 31.466102 | 192 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/fast_weights/token_wise.py | """
---
title: Fast Weight Systems
summary: >
This is an annotated implementation/tutorial of
Linear Transformers Are Secretly Fast Weight Memory Systems in PyTorch.
---
"""
from typing import Optional
import torch
from torch import nn
from labml_helpers.module import Module
from labml_nn.transformers.fast_weight... | 4,216 | 31.19084 | 95 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/fast_weights/__init__.py | """
---
title: Linear Transformers Are Secretly Fast Weight Memory Systems
summary: >
This is an annotated implementation/tutorial of
Linear Transformers Are Secretly Fast Weight Memory Systems in PyTorch.
---
# Fast weights transformer
The paper
[Linear Transformers Are Secretly Fast Weight Memory Systems in PyT... | 12,989 | 38.483283 | 192 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/gmlp/experiment.py | """
---
title: Pay Attention to MLPs (gMLP) Experiment
summary: This experiment trains a gMLP based model on Tiny Shakespeare dataset.
---
# [Pay Attention to MLPs (gMLP)](index.html) Experiment
This is an annotated PyTorch experiment to train a [gMLP model](index.html).
The paper also applies a Stochastic Depth reg... | 3,281 | 27.293103 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/gmlp/__init__.py | """
---
title: Pay Attention to MLPs (gMLP)
summary: >
This is an annotated implementation/tutorial of Pay Attention to MLPs (gMLP) in PyTorch.
---
# Pay Attention to MLPs (gMLP)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Pay Attention to MLPs](https://papers.labml.ai/paper/2105.08050).
... | 6,152 | 37.45625 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/knn/train_model.py | """
---
title: Train Autoregressive Transformer
summary: This is training code with notes for a basic auto-regressive transformer.
---
# Train Autoregressive Transformer
This trains a simple [transformer](../../) model for auto-regression.
"""
import torch
from labml import experiment
from labml.configs import optio... | 4,481 | 29.910345 | 96 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/knn/eval_knn.py | """
---
title: Evaluate k-nearest neighbor language model
summary: >
This runs the kNN model and merges the kNN results with transformer output to
achieve better results than just using the transformer.
---
# Evaluate k-nearest neighbor language model
"""
from typing import Optional, List
import faiss
import nump... | 5,907 | 36.392405 | 120 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/knn/__init__.py | """
---
title: k-Nearest Neighbor Language Models
summary: >
This is a simple PyTorch implementation/tutorial of the paper
Generalization through Memorization: Nearest Neighbor Language Models using FAISS.
It runs a kNN model on the final transformer layer embeddings to improve the
loss of transformer based lan... | 1,934 | 42.977273 | 107 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/knn/build_index.py | """
---
title: Build FAISS index for k-NN search
summary: This builds the FAISS index with the transformer embeddings.
---
# Build FAISS index for k-NN search
We want to build the index of $\big(f(c_i), w_i\big)$.
We store $f(c_i)$ and $w_i$ in memory mapped numpy arrays.
We find $f(c_i)$ nearest to $f(c_t)$ using [F... | 5,712 | 35.388535 | 120 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/xl/experiment.py | """
---
title: Transformer XL Experiment
summary: This experiment trains a transformer XL model on tiny Shakespeare dataset.
---
# Transformer XL Experiment
This is an annotated PyTorch experiment to train a transformer xl model.
"""
from typing import List
import torch
import torch.nn as nn
from labml.logger import... | 8,779 | 32.257576 | 102 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/xl/__init__.py | """
---
title: Transformer XL
summary: >
Documented implementation with explanations of a
Transformer-XL model.
---
# Transformer XL
This is an implementation of
[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860)
in [PyTorch](https://pytorch.org).
Transfor... | 5,422 | 36.923077 | 182 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/xl/relative_mha.py | """
---
title: Relative Multi-Headed Attention
summary: >
Documented implementation with explanations of
Relative Multi-Headed Attention from paper Transformer-XL.
---
# Relative Multi-Headed Attention
This is an implementation of relative multi-headed attention from paper
[Transformer-XL: Attentive Language Mode... | 6,230 | 39.72549 | 110 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/basic/autoregressive_experiment.py | """
---
title: Transformer Auto-Regression Experiment
summary: >
This trains a simple transformer model on NLP auto-regression.
---
# Transformer Auto-Regression Experiment
This trains a simple transformer introduced in [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
on an NLP auto-regression task (wi... | 4,430 | 27.587097 | 110 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/switch/experiment.py | """
---
title: Switch Transformer Experiment
summary: This experiment trains a small switch transformer on tiny Shakespeare dataset.
---
# Switch Transformer Experiment
This is an annotated PyTorch experiment to train a switch transformer.
"""
import torch
import torch.nn as nn
from labml import experiment, tracker... | 8,280 | 34.088983 | 111 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/switch/__init__.py | """
---
title: Switch Transformer
summary: >
This is an annotated implementation/tutorial a miniature version of Switch Transformer in PyTorch.
---
# Switch Transformer
This is a miniature [PyTorch](https://pytorch.org) implementation of the paper
[Switch Transformers: Scaling to Trillion Parameter Models with Simp... | 9,937 | 40.932489 | 186 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/fnet/experiment.py | """
---
title: FNet Experiment
summary: This experiment trains a FNet based model on AG News dataset.
---
# [FNet](index.html) Experiment
This is an annotated PyTorch experiment to train a [FNet model](index.html).
This is based on
[general training loop and configurations for AG News classification task](../../expe... | 4,328 | 26.75 | 118 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/fnet/__init__.py | """
---
title: "FNet: Mixing Tokens with Fourier Transforms"
summary: >
This is an annotated implementation/tutorial of FNet in PyTorch.
---
# FNet: Mixing Tokens with Fourier Transforms
This is a [PyTorch](https://pytorch.org) implementation of the paper
[FNet: Mixing Tokens with Fourier Transforms](https://arxiv.... | 3,492 | 36.967391 | 120 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/feedback/experiment.py | """
---
title: Train Feedback Transformer
summary: This is training code with notes for a feedback transformer.
---
# Train Feedback Transformer
This trains a [feedback transformer](index.html) model for auto-regression.
You can pick the original feedback transformer or the new version
where the keys and values are p... | 4,885 | 33.167832 | 188 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/feedback/__init__.py | """
---
title: Feedback Transformer
summary: >
This is an annotated implementation/tutorial the Feedback Transformer in PyTorch.
---
# Feedback Transformer
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Accessing Higher-level Representations in Sequential Transformers with Feedback Memory](ht... | 19,846 | 36.376648 | 188 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/gpt/__init__.py | """
---
title: GPT
summary: >
Implementation/tutorial of GPT model and training code.
---
# GPT
This is a tutorial/implementation of
[OpenAI GPT architecture](https://openai.com/blog/better-language-models/)
in [PyTorch](https://pytorch.org).
We got a bunch of implementation details from
[minGPT](https://github.com... | 8,665 | 31.456929 | 183 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/mlm/experiment.py | """
---
title: Masked Language Model Experiment
summary: This experiment trains Masked Language Model (MLM) on Tiny Shakespeare dataset.
---
# [Masked Language Model (MLM)](index.html) Experiment
This is an annotated PyTorch experiment to train a [Masked Language Model](index.html).
"""
from typing import List
impor... | 10,183 | 31.641026 | 110 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/mlm/__init__.py | """
---
title: Masked Language Model
summary: >
This is an annotated implementation/tutorial of the Masked Language Model in PyTorch.
---
# Masked Language Model (MLM)
This is a [PyTorch](https://pytorch.org) implementation of the Masked Language Model (MLM)
used to pre-train the BERT model introduced in the paper... | 6,396 | 44.049296 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/compressive/experiment.py | """
---
title: Compressive Transformer Experiment
summary: This experiment trains a compressive transformer model on tiny Shakespeare dataset.
---
# Compressive Transformer Experiment
This is an annotated PyTorch experiment to train a compressive transformer model.
"""
from typing import List, Tuple, NamedTuple
impo... | 13,020 | 35.886686 | 120 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/compressive/__init__.py | """
---
title: Compressive Transformer
summary: >
Documented implementation with explanations of a
Compressive Transformer model.
---
# Compressive Transformer
This is an implementation of
[Compressive Transformers for Long-Range Sequence Modelling](https://arxiv.org/abs/1911.05507)
in [PyTorch](https://pytorch.o... | 13,740 | 39.774481 | 191 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/vit/experiment.py | """
---
title: Train a Vision Transformer (ViT) on CIFAR 10
summary: >
Train a Vision Transformer (ViT) on CIFAR 10
---
# Train a [Vision Transformer (ViT)](index.html) on CIFAR 10
[](https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f)
""... | 2,861 | 28.505155 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/vit/__init__.py | """
---
title: Vision Transformer (ViT)
summary: >
A PyTorch implementation/tutorial of the paper
"An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale"
---
# Vision Transformer (ViT)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[An Image Is Worth 16x16 Words: Transfor... | 8,071 | 36.198157 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/mlp_mixer/experiment.py | """
---
title: MLP Mixer experiment
summary: This experiment trains MLP Mixer on Tiny Shakespeare dataset.
---
# [MLP Mixer](index.html) Experiment
This is an annotated PyTorch experiment to train a [MLP Mixer Model](index.html).
"""
from labml import experiment
from labml.configs import option
from labml_nn.transfo... | 3,030 | 25.12931 | 86 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/mlp_mixer/__init__.py | """
---
title: "MLP-Mixer: An all-MLP Architecture for Vision"
summary: >
This is an annotated implementation/tutorial of MLP-Mixer: An all-MLP Architecture for Vision in PyTorch.
---
# MLP-Mixer: An all-MLP Architecture for Vision
This is a [PyTorch](https://pytorch.org) implementation of the paper
[MLP-Mixer: An ... | 2,956 | 35.506173 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/glu_variants/experiment.py | """
---
title: Gated Linear Units and Variants
summary: >
Train an auto-regressive transformer with Gated Linear Units and variants
for the position-wise feedforward network (FFN).
---
# Gated Linear Units and Variants
This trains a simple [transformer](../../) model for auto-regression.
We try different variants... | 4,296 | 31.067164 | 100 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/transformers/glu_variants/simple.py | """
---
title: Gated Linear Units and Variants
summary: >
Train an auto-regressive transformer with Gated Linear Units and variants
for the position-wise feedforward network (FFN).
---
# Gated Linear Units and Variants
This trains a simple [transformer](../../) model for auto-regression.
We try different variants... | 11,935 | 37.503226 | 188 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/distillation/large.py | """
---
title: Train a large model on CIFAR 10
summary: >
Train a large model on CIFAR 10 for distillation.
---
# Train a large model on CIFAR 10
This trains a large model on CIFAR 10 for [distillation](index.html).
[](https://app.labml.ai/run/... | 2,558 | 26.815217 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/distillation/small.py | """
---
title: Train a small model on CIFAR 10
summary: >
Train a small model on CIFAR 10 to test how much distillation benefits.
---
# Train a small model on CIFAR 10
This trains a small model on CIFAR 10 to test how much [distillation](index.html) benefits.
[![View Run](https://img.shields.io/badge/labml-experi... | 2,498 | 27.078652 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/distillation/__init__.py | """
---
title: Distilling the Knowledge in a Neural Network
summary: >
PyTorch implementation and tutorial of the paper
Distilling the Knowledge in a Neural Network.
---
# Distilling the Knowledge in a Neural Network
This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
[Distilling the Kno... | 8,572 | 33.849593 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/dcgan/__init__.py | """
---
title: Deep Convolutional Generative Adversarial Networks (DCGAN)
summary: A simple PyTorch implementation/tutorial of Deep Convolutional Generative Adversarial Networks (DCGAN).
---
# Deep Convolutional Generative Adversarial Networks (DCGAN)
This is a [PyTorch](https://pytorch.org) implementation of paper
[... | 3,894 | 31.190083 | 129 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/original/experiment.py | """
---
title: Generative Adversarial Networks experiment with MNIST
summary: This experiment generates MNIST images using multi-layer perceptron.
---
# Generative Adversarial Networks experiment with MNIST
"""
from typing import Any
import torch
import torch.nn as nn
import torch.utils.data
from torchvision import ... | 7,964 | 30.113281 | 116 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/original/__init__.py | """
---
title: Generative Adversarial Networks (GAN)
summary: A simple PyTorch implementation/tutorial of Generative Adversarial Networks (GAN) loss functions.
---
# Generative Adversarial Networks (GAN)
This is an implementation of
[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
The generator, $... | 4,927 | 37.80315 | 120 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/stylegan/experiment.py | """
---
title: StyleGAN 2 Model Training
summary: >
An annotated PyTorch implementation of StyleGAN2 model training code.
---
# [StyleGAN 2](index.html) Model Training
This is the training code for [StyleGAN 2](index.html) model.
*<small>These are $64 \times 64$ images generat... | 17,574 | 36.553419 | 130 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/stylegan/__init__.py | """
---
title: StyleGAN 2
summary: >
An annotated PyTorch implementation of StyleGAN2.
---
# StyleGAN 2
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Analyzing and Improving the Image Quality of StyleGAN](https://arxiv.org/abs/1912.04958)
which introduces **StyleGAN 2**.
StyleGAN 2 is an im... | 36,581 | 37.588608 | 118 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/wasserstein/__init__.py | r"""
---
title: Wasserstein GAN (WGAN)
summary: A simple PyTorch implementation/tutorial of Wasserstein Generative Adversarial Networks (WGAN) loss functions.
---
# Wasserstein GAN (WGAN)
This is an implementation of
[Wasserstein GAN](https://arxiv.org/abs/1701.07875).
The original GAN loss is based on Jensen-Shanno... | 4,738 | 33.591241 | 182 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/wasserstein/gradient_penalty/experiment.py | """
---
title: WGAN-GP experiment with MNIST
summary: This experiment generates MNIST images using convolutional neural network.
---
# WGAN-GP experiment with MNIST
"""
import torch
from labml import experiment, tracker
# Import configurations from [Wasserstein experiment](../experiment.html)
from labml_nn.gan.wasse... | 2,780 | 30.965517 | 120 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/wasserstein/gradient_penalty/__init__.py | r"""
---
title: Gradient Penalty for Wasserstein GAN (WGAN-GP)
summary: >
An annotated PyTorch implementation/tutorial of
Improved Training of Wasserstein GANs.
---
# Gradient Penalty for Wasserstein GAN (WGAN-GP)
This is an implementation of
[Improved Training of Wasserstein GANs](https://arxiv.org/abs/1704.00028... | 2,822 | 32.607143 | 115 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/gan/cycle_gan/__init__.py | """
---
title: Cycle GAN
summary: >
A simple PyTorch implementation/tutorial of Cycle GAN introduced in paper
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.
---
# Cycle GAN
This is a [PyTorch](https://pytorch.org) implementation/tutorial of the paper
[Unpaired Image-to-Image Tran... | 28,903 | 36.537662 | 180 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/resnet/experiment.py | """
---
title: Train a ResNet on CIFAR 10
summary: >
Train a ResNet on CIFAR 10
---
# Train a [ResNet](index.html) on CIFAR 10
[](https://app.labml.ai/run/fc5ad600e4af11ebbafd23b8665193c1)
"""
from typing import List, Optional
from torch import ... | 2,238 | 25.341176 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/resnet/__init__.py | """
---
title: Deep Residual Learning for Image Recognition (ResNet)
summary: >
A PyTorch implementation/tutorial of Deep Residual Learning for Image Recognition (ResNet).
---
# Deep Residual Learning for Image Recognition (ResNet)
This is a [PyTorch](https://pytorch.org) implementation of the paper
[Deep Residual L... | 13,477 | 40.343558 | 131 | py |
robust-OT | robust-OT-main/robust StyleGAN 2/labml_nn/activations/swish.py | import torch
from torch import nn
from labml_helpers.module import Module
class Swish(Module):
def __init__(self):
super().__init__()
self.sigmoid = nn.Sigmoid()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x * self.sigmoid(x)
| 277 | 18.857143 | 55 | py |
aiida-yambo | aiida-yambo-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# aiida-wannier90 documentation build configuration file, created by
# sphinx-quickstart on Fri Oct 10 02:14:52 2014.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated fil... | 12,954 | 32.389175 | 271 | py |
pymanoid | pymanoid-master/doc/src/conf.py | # -*- coding: utf-8 -*-
#
# pymanoid documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 13 14:41:18 2017.
import os
import sys
sys.path.insert(0, os.path.abspath("../.."))
sys.path.insert(0, os.path.abspath("../../examples"))
sys.path.insert(0, os.path.abspath("../../examples/contact_st... | 8,576 | 27.685619 | 79 | py |
metaMIMIC | metaMIMIC-main/4_metaMIMIC_columns/metaMIMIC_columns.py | ### metaMIMIC columns
# Below code is supposed to be run using the CSV file created with 'metaMIMIC data script'.
import os
import numpy as np
import pandas as pd
import xgboost as xgb
import dalex as dx
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score, StratifiedKFold
## S... | 2,291 | 34.8125 | 106 | py |
metaMIMIC | metaMIMIC-main/6_metaMIMIC_experiment_bayes/metaMIMIC_experiment_bayes.py | ### metaMIMIC experiment 3
import os, time
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
import xgboost as xgb
from sklearn.model_selection import StratifiedKFold
import scipy, skopt
if os.path.isfile('./results.csv'):
print('Results file already exists, aborting execution.')
... | 3,747 | 41.11236 | 414 | py |
metaMIMIC | metaMIMIC-main/5_metaMIMIC_experiment_3/metaMIMIC_experiment_3.py | ### metaMIMIC experiment 4
import os, time
import pandas as pd
import xgboost as xgb
from sklearn.model_selection import StratifiedKFold, cross_val_score
param_sets_raw = pd.read_csv('./grid.csv').drop(['param_index', 'missing'], axis=1).iloc[1:,].to_dict('records')
param_sets = []
for param_set in param_sets_raw:
... | 1,929 | 41.888889 | 160 | py |
metaMIMIC | metaMIMIC-main/3_metaMIMIC_experiment_2/metaMIMIC_experiment_2.py | ### metaMIMIC experiment 2
import time, os.path
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
import xgboost as xgb
from sklearn.model_selection import StratifiedKFold, cross_val_score
if os.path.isfile('./results.csv'):
print('Results file already exists, aborting execution.')
... | 2,907 | 48.288136 | 150 | py |
metaMIMIC | metaMIMIC-main/2_metaMIMIC_experiment_1/metaMIMIC_experiment_1.py | ### metaMIMIC experiment 1
import time, os.path
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
import xgboost as xgb
from sklearn.model_selection import StratifiedKFold, cross_val_score
data = pd.read_csv('../1_metaMIMIC_data/metaMIMIC.csv')
if os.path.isfile('./results.csv'):
pr... | 1,777 | 41.333333 | 145 | py |
unified-generative-zoo | unified-generative-zoo-main/main.py | import logging
import os
import torch
import datasets
import transformers
from transformers import (
HfArgumentParser,
set_seed,
)
from utils.config_utils import get_config
from utils.program_utils import get_model, get_preprocessor, get_evaluator, get_visualizer
from preprocess.to_model import get_multi_task_... | 4,296 | 28.840278 | 109 | py |
unified-generative-zoo | unified-generative-zoo-main/generate.py | import torch
import argparse
import logging
import os
from PIL import Image
import json
from tqdm import tqdm
from utils.config_utils import get_config
from model.gan_wrapper.get_gan_wrapper import get_gan_wrapper
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser(descriptio... | 1,773 | 25.088235 | 103 | py |
unified-generative-zoo | unified-generative-zoo-main/trainer/trainer.py | import os
from pathlib import Path
import time
import datetime
import json
import re
import logging
import warnings
import random
import math
import collections.abc
import shutil
from typing import Dict, Union, Any, Optional, List, Tuple
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import... | 47,834 | 41.709821 | 129 | py |
unified-generative-zoo | unified-generative-zoo-main/evaluation/utils.py | import numpy as np
import torch
import cv2
from torchvision import utils
def save_image(image_path, image):
assert image.dim() == 3 and image.shape[0] == 3
utils.save_image(image, image_path)
def ssim(img1, img2):
assert img1.shape == img2.shape
assert img1.ndim == 2 and img2.ndim == 2
C1 = (0.... | 1,123 | 29.378378 | 89 | py |
unified-generative-zoo | unified-generative-zoo-main/evaluation/clip_coverage.py | import torch
from tqdm import tqdm
import torch.nn.functional as F
import lpips
class Evaluator(object):
def __init__(self, args, meta_args):
self.args = args
self.meta_args = meta_args
self.lpips_loss = lpips.LPIPS(net='vgg').cuda()
def evaluate(self, images, model, weighted_loss,... | 2,776 | 33.283951 | 156 | py |
unified-generative-zoo | unified-generative-zoo-main/evaluation/multi_task.py | import os
import numpy as np
import torch
from utils.program_utils import get_evaluator
from utils.config_utils import get_config
class Evaluator(object):
def __init__(self, meta_args):
self.meta_args = meta_args
def evaluate(self, images, model, weighted_loss, losses, dataset, split):
assert... | 2,818 | 38.152778 | 94 | py |
unified-generative-zoo | unified-generative-zoo-main/preprocess/to_model.py | import math
from typing import Dict
from copy import deepcopy
import numpy as np
from pprint import pprint
from random import shuffle
from torch.utils.data import Dataset
def upsample(data, weight):
n_data = len(data)
assert weight >= 1
integral = list(range(n_data)) * int(math.floor(weight))
residua... | 4,981 | 32.436242 | 97 | py |
unified-generative-zoo | unified-generative-zoo-main/preprocess/empty_small_eval.py | import torch
from datasets import DatasetDict
from torch.utils.data import Dataset
class Preprocessor(object):
def __init__(self, args, meta_args):
self.args = args
self.meta_args = meta_args
def preprocess(self, raw_datasets: DatasetDict, cache_root: str):
assert len(raw_datasets) =... | 2,128 | 24.650602 | 99 | py |
unified-generative-zoo | unified-generative-zoo-main/preprocess/empty_256.py | import torch
from datasets import DatasetDict
from torch.utils.data import Dataset
class Preprocessor(object):
def __init__(self, args, meta_args):
self.args = args
self.meta_args = meta_args
def preprocess(self, raw_datasets: DatasetDict, cache_root: str):
assert len(raw_datasets) =... | 2,130 | 24.674699 | 99 | py |
unified-generative-zoo | unified-generative-zoo-main/visualization/single_image.py | import os
import math
from utils.file_utils import save_images
import torch.nn.functional as F
class Visualizer(object):
def __init__(self, args):
self.args = args
def visualize(self,
images,
model,
description: str,
save_dir: ... | 1,060 | 20.653061 | 46 | py |
unified-generative-zoo | unified-generative-zoo-main/visualization/single_image_8.py | import os
import math
from utils.file_utils import save_images
import torch.nn.functional as F
class Visualizer(object):
def __init__(self, args):
self.args = args
def visualize(self,
images,
model,
description: str,
save_dir: ... | 923 | 19.086957 | 45 | py |
unified-generative-zoo | unified-generative-zoo-main/utils/dist_utils.py | import torch
import numpy as np
def truncated_gumbel(logit, truncation):
"""truncated_gumbel
:param logit: Location of the Gumbel variable (e.g., log probability)
:param truncation: Value of Maximum Gumbel
"""
# Note: In our code, -inf shows up for zero-probability events, which is
# handled i... | 2,466 | 33.263889 | 117 | py |
unified-generative-zoo | unified-generative-zoo-main/utils/file_utils.py | import os
import blobfile as bf
import torch
from torchvision import utils
from PIL import Image
def save_images(images: torch.Tensor, output_dir: str, file_prefix: str, nrows: int, iteration: int) -> None:
utils.save_image(
images,
os.path.join(output_dir, f"{file_prefix}_{str(iteration).zfill(6... | 1,005 | 27.742857 | 109 | py |
unified-generative-zoo | unified-generative-zoo-main/model/langevin_dynamics.py | import torch
import torch.nn as nn
import numpy as np
from .model_utils import requires_grad, MAX_SAMPLE_SIZE
from .gan_wrapper.get_gan_wrapper import get_gan_wrapper
from .energy.get_energy import get_energy, parse_key
class LangevinDynamics(nn.Module):
def __init__(self, args):
super(LangevinDynamics,... | 5,464 | 33.808917 | 101 | py |
unified-generative-zoo | unified-generative-zoo-main/model/energy/clip_guide.py | import torch
import torch.nn as nn
import torchvision.transforms as transforms
import clip
from ..model_utils import requires_grad
from ..lib.diffaug.DiffAugment_pytorch import DiffAugment
POLICY = 'color,translation,resize,cutout'
class CLIPEnergy(nn.Module):
def __init__(self, text, clip_models, clip_model_w... | 3,599 | 28.268293 | 109 | py |
unified-generative-zoo | unified-generative-zoo-main/model/energy/class_condition.py | from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..lib.celeba.classifier import Classifier
from ..model_utils import requires_grad
class ClassEnergy(nn.Module):
def __init__(self, classes, binaries, weights):
super(ClassEnergy, self).__init__()
... | 1,581 | 26.754386 | 66 | py |
unified-generative-zoo | unified-generative-zoo-main/model/energy/prior_z.py | import torch.nn as nn
class PriorZEnergy(nn.Module):
def __init__(self):
super(PriorZEnergy, self).__init__()
@ staticmethod
def prepare_inputs(**kwargs):
return {
'z': kwargs['z'],
}
def forward(self, z):
if z.ndim == 2:
prior_z_loss = 0.5 * (... | 491 | 20.391304 | 55 | py |
unified-generative-zoo | unified-generative-zoo-main/model/energy/id_single.py | import torch
from torch import nn
from PIL import Image
from torchvision.transforms import ToTensor, Compose, Resize
from ..model_utils import requires_grad
from ..lib.id_recognition.model_irse import Backbone
RESOLUTION = 112 # Resolution depends on the center crop AND 256 below.
class IDSingleEnergy(nn.Module):
... | 2,211 | 29.30137 | 110 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/renderer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Wrap the generator to render a sequence of images"""
import torch
import torch.nn.functional as F
import numpy as np
from torch import random
import tqdm
import copy
import trimesh
class Renderer(object):
def __init__(self, generator, di... | 3,534 | 39.170455 | 130 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/legacy.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 16,511 | 50.439252 | 154 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/stylenerf.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import grad
from training.networks import *
from dnnlib.camera import *
from dnnlib.geometry import (
positional_encoding, upsampl... | 115,584 | 47.728921 | 148 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/augment.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | 26,372 | 60.048611 | 366 | py |
unified-generative-zoo | unified-generative-zoo-main/model/lib/stylenerf/training/data_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import PIL.Image
import torch
import cv2, albumentations
import numpy as np
def save_image(img, filename):
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f... | 1,253 | 31.153846 | 89 | py |
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