Linh T. Duong linhduongtuan

Using TPU VM instance w/ pre-alpha timm bits setup as per: https://github.com/rwightman/pytorch-image-models/tree/bits_and_tpu/timm/bits#readme

python3 launch_xla.py --num-devices 8 train.py gs://my-imagenet --config hparams.yaml

Note the config yaml files do have args that are not used or active based on other overriding code or the state of the current training code. The bits code is under heavy development so these configs will likely need specific revision (currently https://github.com/rwightman/pytorch-image-models/commit/5e95ced5a7763541f7219f35fd155e3fbfe66e8b)

The gMlp hparams are the last (latest) in the series and likely will produce better results than the earlier gmixer / resmlp variants...

Note, for adapting the LR to differenrt batch size. AdamW is being used here and I use a sqrt scaling for the learning rate wrt to (global) batch size. I typicall use linear LR scaling w/ SGD or RMSProp for most from-scratch training.

ResNet-101 in Keras

This is an Keras implementation of ResNet-101 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.

ResNet Paper:

Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385

	nodes:
	- id: webcam
	custom:
	source: https://huggingface.co/datasets/dora-rs/dora-idefics2/raw/main/operators/opencv_stream.py
	outputs:
	- image
	- id: idefics2
	operator:
	python: https://huggingface.co/datasets/dora-rs/dora-idefics2/raw/main/operators/idefics2_op.py
	inputs:

	""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory
	git clone https://github.com/myshell-ai/OpenVoice
	cd OpenVoice
	git clone https://huggingface.co/myshell-ai/OpenVoice
	cp -r OpenVoice/* .
	pip install whisper pynput pyaudio
	"""

	from openai import OpenAI
	import time

	aa: null
	amp: false
	aug_splits: 0
	batch_size: 256
	bn_eps: null
	bn_momentum: null
	bn_tf: false
	channels_last: false
	checkpoint_hist: 10
	clip_grad: 1.0

	aa: rand-m6-n5-inc1-mstd1.0
	amp: false
	apex_amp: false
	aug_splits: 0
	batch_size: 256
	bn_eps: null
	bn_momentum: null
	bn_tf: false
	channels_last: false
	checkpoint_hist: 10

	""" A simple U-Net w/ timm backbone encoder

	Based off an old version of Unet in https://github.com/qubvel/segmentation_models.pytorch

	Hacked together by Ross Wightman
	"""

	from typing import Optional, List

	import torch

	import torch.utils.data as data
	import os
	import re
	import torch
	import tarfile
	from PIL import Image


	IMG_EXTENSIONS = ['.png', '.jpg', '.jpeg']

	import torch
	from torch import nn
	import torch.nn.functional as F
	from collections import OrderedDict
	import math

	def pdist(v):
	dist = torch.norm(v[:, None] - v, dim=2, p=2)
	return dist