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ptrblck

pytorch_misc

Code snippets created for the PyTorch discussion board

TutorialsTool-specific tutorialsPython
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创建于 2018-11-15 · 更新于 2026-10-08 · 今日第 16834 名
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ptrblck

ptrblck/pytorch_misc 排名第 16,834，有 570 个星标。

同类别比较

ptrblck/pytorch_misc 排名第 16,834，此处有 8 个同类条目；carlvellotti/claude-code-everyone-course 排名第 16,826。

同类别比较
名称排名指标
ayaka14732/tpu-starter16,782
语言:
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星标数:
572
分叉数:
31
简介:
Everything you want to know about Google Cloud TPU
demuxin/pytorch_tricks16,784
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572
分叉数:
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简介:
some tircks for PyTorch
jbagnato/machine-learning16,814
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星标数:
571
分叉数:
729
简介:
Código Python, Jupyter Notebooks, archivos csv con ejemplos para los ejercicios del Blog aprendemachinelearning.com y del libro Aprende Machine Learning en Español
carlvellotti/claude-code-everyone-course16,826
语言:
MDX
星标数:
570
分叉数:
86
简介:
Claude Code for Everyone - Learn Claude Code in Claude Code
huggingface/llm_training_handbook16,854
语言:
Python
星标数:
569
分叉数:
46
简介:
An open collection of methodologies to help with successful training of large language models.
qx0731/Sharing_ISL_python16,873
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星标数:
568
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272
简介:
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shawroad/NLP_pytorch_project16,892
语言:
Python
星标数:
567
分叉数:
121
简介:
Embedding, NMT, Text_Classification, Text_Generation, NER etc.
alexeygrigorev/mlbookcamp-code16,899
语言:
Jupyter Notebook
星标数:
566
分叉数:
187
简介:
The code from the Machine Learning Bookcamp book
关键参数与规格
关键参数与规格
参数值
语言Python
类别Tutorials
子类别Tool-specific tutorials
关键词Computer vision, Data processing, Datasets, Model architecture
星标数570
分叉数66
贡献者1
创建日期2018-11-15
更新日期2026-10-08
1 天星标数0
7 天星标数0
排名16,834
上次排名16,626
排名变化-208
7 天星标排名15,752
上次 7 天星标排名15,761
7 天星标排名变化9
总星标排名17,491
上次总星标排名17,461
总星标排名变化-30
README 摘录

PyTorch misc Collection of code snippets I've written for the PyTorch discussion board. All scripts were testes using the PyTorch 1. Some scripts might be a good starter to create a tutorial. Overview accumulate_gradients - Comparison of accumulated gradients/losses to vanilla batch update. adaptive_batchnorm- Adaptive BN implementation using two additional parameters: . adaptive_pooling_torchvision - Example of using adaptive pooling layers in pretrained models to use different spatial input shapes. batch_norm_manual - Comparison of PyTorch BatchNorm layers and

a manual calculation. change_crop_in_dataset - Change the image crop size on the fly using a Dataset. channel_to_patches - Permute image data so that channel values of each pixel are flattened to an image patch around the pixel. conv_rnn - Combines a 3DCNN with an RNN; uses windowed frames as inputs. csv_chunk_read - Provide data chunks from continuous . densenet_forwardhook - Use forward hooks to get intermediate activations from . Uses separate modules to process these activations further. edge

_weighting_segmentation - Apply weighting to edges for a segmentation task. image_rotation_with_matrix - Rotate an image given an angle using 1. LocallyConnected2d - Implementation of a locally connected 2d layer. mnist_autoencoder - Simple autoencoder for MNIST data. Includes visualizations of output images, intermediate activations and conv kernels. mnist_permuted - MNIST training using permuted pixel locations. model_sharding_data_parallel - Model sharding with using 2 pairs of 2 GPUs. momentum_update_nograd - Script to see how parameters are updated when an

optimizer is used with momentum/running estimates, even if gradients are zero. pytorch_redis - Script to demonstrate the loading data from redis using a PyTorch Dataset and DataLoader. shared_array - Script to demonstrate the usage of shared arrays using multiple workers. shared_dict - Script to demonstrate the usage of shared dicts using multiple workers. weighted_sampling - Usage of WeightedRandomSampler using an imbalanced dataset with class imbalance 99 to 1.

常见问题

记录了多少星标和分叉？

ptrblck/pytorch_misc 有 570 个星标和 66 个分叉。

仓库排名是多少？

ptrblck/pytorch_misc 排名第 16,834。

记录的语言和类别是什么？

ptrblck/pytorch_misc 有 570 个星标，语言为 Python，类别为 Tutorials。

哪个仓库排名最接近？

ptrblck/pytorch_misc 与 carlvellotti/claude-code-everyone-course 比较，后者排名第 16,826，有 570 个星标。

README

PyTorch misc

Collection of code snippets I've written for the PyTorch discussion board.

All scripts were testes using the PyTorch 1.0 preview and torchvision 0.2.1.

Additional libraries, e.g. numpy or pandas, are used in a few scripts.

Some scripts might be a good starter to create a tutorial.

Overview

  • accumulate_gradients - Comparison of accumulated gradients/losses to vanilla batch update.
  • adaptive_batchnorm- Adaptive BN implementation using two additional parameters: out = a * x + b * bn(x).
  • adaptive_pooling_torchvision - Example of using adaptive pooling layers in pretrained models to use different spatial input shapes.
  • batch_norm_manual - Comparison of PyTorch BatchNorm layers and a manual calculation.
  • change_crop_in_dataset - Change the image crop size on the fly using a Dataset.
  • channel_to_patches - Permute image data so that channel values of each pixel are flattened to an image patch around the pixel.
  • conv_rnn - Combines a 3DCNN with an RNN; uses windowed frames as inputs.
  • csv_chunk_read - Provide data chunks from continuous .csv file.
  • densenet_forwardhook - Use forward hooks to get intermediate activations from densenet121. Uses separate modules to process these activations further.
  • edge_weighting_segmentation - Apply weighting to edges for a segmentation task.
  • image_rotation_with_matrix - Rotate an image given an angle using 1.) a nested loop and 2.) a rotation matrix and mesh grid.
  • LocallyConnected2d - Implementation of a locally connected 2d layer.
  • mnist_autoencoder - Simple autoencoder for MNIST data. Includes visualizations of output images, intermediate activations and conv kernels.
  • mnist_permuted - MNIST training using permuted pixel locations.
  • model_sharding_data_parallel - Model sharding with DataParallel using 2 pairs of 2 GPUs.
  • momentum_update_nograd - Script to see how parameters are updated when an optimizer is used with momentum/running estimates, even if gradients are zero.
  • pytorch_redis - Script to demonstrate the loading data from redis using a PyTorch Dataset and DataLoader.
  • shared_array - Script to demonstrate the usage of shared arrays using multiple workers.
  • shared_dict - Script to demonstrate the usage of shared dicts using multiple workers.
  • unet_demo - Simple UNet demo.
  • weighted_sampling - Usage of WeightedRandomSampler using an imbalanced dataset with class imbalance 99 to 1.

Feedback is very welcome!

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